Analysis of Germination Curves of Cinchona officinalis L. (Rubiaceae) Using Sigmoidal Mathematical Models

IF 1.5 Q2 AGRONOMY International Journal of Agronomy Pub Date : 2023-01-09 DOI:10.1155/2023/1360608
L. Quiñones-Huatangari, A. E. Huaccha-Castillo, F. H. Fernandez-Zarate, Eli Morales-Rojas, Jenny Del Milagro Marrufo-Jiménez, Leslie Lizbeth Mejía-Córdova
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Abstract

Seed germination is the fundamental phenomenon that determines the successful growth and development of each plant species, even more so in Cinchona officinalis, which is a forest species that stands out for its medicinal importance. The objective of this work was to determine the best sigmoidal mathematical model describing the germination of C. officinalis. For the germination test, a completely randomized design was used with six treatments and three replicates per treatment; 100°C. officinalis seeds were used per replicate, and 1800 seeds were needed in the trial. Gompertz sigmoidal, logistic, and von Bertalanffy models were used to analyse the germination curves of C. officinalis. The results of these adjustments were analysed based on the graphic representation and statistical criteria (Akaike’s value ( A I C ), R 2 , and R a i 2 ). The results suggest that the Gompertz and logistic models have a better graphic representation, showing values close to those observed, while the von Bertalanffy model shows negative germination values. According to the statistical criteria, the lowest AIC and the highest were obtained. R 2 and R a i 2 with the Gompertz model, followed by the logistic model and von Bertalanffy. It is concluded that the Gompertz model can represent the shape of the germination curves of C. officinalis for the six treatments of the test.
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金鸡纳(金鸡纳科)萌发曲线的s型数学模型分析
种子发芽是决定每个植物物种成功生长和发育的基本现象,金鸡纳更是如此,这是一种具有重要药用价值的森林物种。本工作的目的是确定最佳的s形数学模型来描述马蹄莲的萌发。发芽试验采用完全随机设计,6个处理,每个处理3个重复;100°C。每个重复使用马蹄草种子,试验需用马蹄草种子1800粒。采用Gompertz s型模型、logistic模型和von Bertalanffy模型分析了黄皮草的萌发曲线。根据图形表示和统计标准(赤池值(A I C)、r2和r2)对这些调整的结果进行分析。结果表明,Gompertz和logistic模型具有更好的图形表示,显示的值与观测值接近,而von Bertalanffy模型显示的是负萌发值。根据统计标准,得到最低AIC和最高AIC。R 2和R i2用Gompertz模型,然后是logistic模型和von Bertalanffy。结果表明,Gompertz模型能较好地反映6个试验处理下马齿苋的萌发曲线形状。
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来源期刊
CiteScore
3.60
自引率
5.30%
发文量
66
审稿时长
16 weeks
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