Determining the Growth of Naked Oat Genotypes by Using Nonlinear Regression Models and Zadoks Growth Scale

Year 2022, Volume: 9 Issue: 4, 1033 - 1041, 18.10.2022

Onur Hocaoğlu , Mevlüt Akçura , Sait Çeri

https://doi.org/10.30910/turkjans.1125748

Abstract

Increasing popularity of the naked oat (Avena nuda L.) in the food industry promoted the value of its cultivation. Despite the growing demand for the naked oat grain, the research about its agronomy is currently limited. Aim of this study were to evaluate the growth characteristics of naked oat with nonlinear regression models. Field trials were conducted according to the split block design with three replications. Our growth data consisted of weekly dry weight observations covering the entire growth span of four naked oat genotypes (211 samplings in total) for two growing seasons. Curve fitting successfully revealed the genotypic and environmental variations when sampling weeks and Zadoks growth stages were used as time measures in two separate analyses. According to results, last week of tillering stage were found to be critical for naked oat when the rate of growth reached its peak around booting stage. Implementing Zadoks growth stage as time measure in growth analysis had several drawbacks but revealed unique interpretations about the crop development and environmental variation. Logistic, Logistic Power and Ratkowsky models were the best fitting models to assess weekly dry weight increases with the coefficient of determinations ranging from 0.99177 to 0.94206.

Keywords

naked oat, nonlinear regression, dry weight, point of inflection, zadoks growth stage

Kavuzsuz yulaf genotiplerinde büyümenin doğrusal olmayan regresyon modelleri ve Zadoks büyüme skalası ile belirlenmesi

Abstract

Kavuzsuz yulafın (Avena nuda L.) gıda endüstrisindeki artan popülerliği tarımının da değerini arttırmaktadır. Kavuzsuz yulaf tanesine yönelik artan talebe karşılık bu bitkinin yetiştiriciliği üzerine yapılan çalışmalar son derece sınırlıdır. Bu çalışma, kavuzsuz yulafın büyüme ve gelişmesinin doğrusal olmayan regresyon yöntemleri ile değerlendirilmesi amacıyla bölünmüş parseller deneme desenine uygun olarak üç tekerrür ile yürütülmüştür. Bu kapsamda iki yıl boyunca kurulan tarla denemelerinde dört kavuzsuz yulaf genotipinin tüm büyüme ve gelişme dönemleri boyunca haftalık olarak toplamda 211 adet örnekleme ile kuru madde birikimleri kaydedilmiştir. Elde edilen büyüme verilerinde eğri uydurma yöntemi uygulanmış; örnekleme haftaları ve Zadoks büyüme dönemleri iki ayrı zaman ölçütü olarak karşılaştırılmıştır. Analiz sonuçları kavuzsuz yulafta kardeşlenme döneminin son haftasının büyüme ve gelişme açısından kritik bir hafta olduğunu, kuru madde birikiminin ise gebeleşme döneminde zirveye ulaştığını göstermiştir. Zadoks büyüme dönemlerinin zaman ölçütü olarak kullanıldığı analizler, belirli zayıf yönleri olmasına rağmen bitki büyümesi ve çevresel faktörlerin etkisi üzerine özgün sonuçlar sağlamıştır. Logistic, Logistic Power ve Ratkowsky modelleri 0,99177 ile 0,94206 arasında değişen belirleme katsayıları ile kavuzsuz yulafta kuru madde birikimini en iyi açıklayan modeller olmuşlardır.

Keywords

Kavuzsuz yulaf, doğrusal olmayan regresyon modelleri, kuru madde, dönüm noktası, Zadoks büyüme skalası

References

• de Bem, C. M., Cargnelutti Filho, A., Chaves, G. G., Kleinpaul, J. A., Pezzini, R. V., & Lavezo, A. 2018. Gompertz and Logistic models to the productive traits of sunn hemp. J. Agric. Sci., 10, 225-238.
• Carini, F., Cargnelutti Filho, A., Pezzini, R. V., Souza, J. M. D., Chaves, G. G., & Procedi, A. 2020. Nonlinear models for describing lettuce growth in autumn-winter. Ciência Rural, 50.
• Diel, M. I., Lúcio, A. D. C., Sari, B. G., Olivoto, T., Pinheiro, M. V. M., Krysczum, D. K., ... & Schmidt, D. 2020. Behavior of strawberry production with growth models: a multivariate approach. Acta Scientiarum. Agronomy, 43.
• Goshu, A. T., Koya, P. R. 2013. Derivation of inflection points of nonlinear regression curves-implications to statistics. Am J Theor Appl Stat, 2(6), 268-272.
• Hao, M., Zhao, X., Wang, Q., Wang, P., Ni, R., Lu, F. 2021. Modelling tree height-diameter relationship of Quercus acutissima in a coniferous and broad-leaved mixed forest in Mount Tai, China. In IOP Conference Series: Earth and Environmental Science, Vol. 791, No. 1, p. 012173. IOP Publishing.
• Hocaoğlu, O., Coşkun, Y. 2018. Evaluation of dry matter accumulation in triticale by different sigmoidal growth models in west anatolia of Turkey. Genetika, 50(2), 561-574.
• Hu, X. Z., Zheng, J. M., Li, X. P., Xu, C., Zhao, Q. 2014. Chemical composition and sensory characteristics of oat flakes: A comparative study of naked oat flakes from China and hulled oat flakes from western countries. Journal of Cereal Science, 60(2): 297-301.
• 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/. Assessed 13 January 2022.
• Juhász, C., Gálya, B., Kovács, E., Nagy, A., Tamás, J., & Huzsvai, L. 2020. Seasonal predictability of weather and crop yield in regions of Central European continental climate. Computers and Electronics in Agriculture, 173, 105400.
• Karadavut, U., Kayis, S. A., Okur, O. 2008. A growth curve application to compare plant heights and dry weights of some wheat varieties. American-Eurasian Journal of Agricultural & Environmental Sciences, 3(6), 888-892.
• Malhi, S. S., Johnston, A. M., Schoenau, J. J., Wang, Z. L., Vera, C. L. 2006. Seasonal biomass accumulation and nutrient uptake of wheat, barley and oat on a Black Chernozem soil in Saskatchewan. Canadian Journal of Plant Science, 86(4), 1005-1014.
• Mello, A. C., Toebe, M., de Souza, R. R., Paraginski, J. A., Somavilla, J. C., Martins, V., & Pinto, A. C. V. 2022. Nonlinear models in the height description of the Rhino sunflower cultivar/Modelos näo lineares na descricäo de altura da cultivar de girassol Rhino. Ciencia Rural, 52(3), 1T-1T.
• Mischan, M. M., Pinho, S. Z. D., Carvalho, L. R. D. 2011. Determination of a point sufficiently close to the asymptote in nonlinear growth functions. Scientia Agricola, 68(1), 109-114.
• Peek, M. S., Russek-Cohen, E., Wait, A. D., Forseth, I. N. 2002. Physiological response curve analysis using nonlinear mixed models. Oecologia, 132(2), 175-180.
• Sari, B. G., Lúcio, A. D. C., Santana, C. S., Olivoto, T., Diel, M. I., Krysczun, D. K. 2019. Nonlinear growth models: An alternative to ANOVA in tomato trials evaluation. European Journal of Agronomy, 104, 21-36.
• Sykut-Domańska, E, Rzedzicki, Z, Nita, Z. 2013. Chemical composition variability of naked and husked oat grain (Avena sativa L.) Cereal Research Communications, 41(2), 327-337.
• Webster, F. 2016. Oats: chemistry and technology. Second Edition. Eds: Webster, F. H.; Wood, P. J. AACC International, Minnesota, U.S.A.
• Wen, Y., Liu, K., Liu, H., Cao, H., Mao, H., Dong, X., Yin, Z. 2019. Comparison of nine growth curve models to describe growth of partridges (Alectoris chukar). Journal of Applied Animal Research. 47:1, 195-200.
• Ye, Y., Wang, G., Huang, Y., Zhu, Y., Meng, Q., Chen, X., Zhang, F., Cui, Z. 2011. Understanding physiological processes associated with yield–trait relationships in modern wheat varieties. Field Crops Research, 124(3), 316-322.
• Yılmaz, E., Çiçek, İ. 2018. Detailed Köppen-Geiger climate regions of Turkey Türkiye’nin detaylandırılmış Köppen-Geiger iklim bölgeleri. Journal of Human Sciences, 15(1), 225-242.
• Zadoks, J. C., Chang, T. T., Konzak, C. F. 1974. A decimal code for the growth stages of cereals. Weed research, 14(6), 415-421.