Allometric equations to predict the leaf area of castor bean cultivars - Ribeiro, João Everthon da Silva; Côelho, Ester Dos Santos; Lopes, Welder de Araújo Rangel; da Silva, Elania Freire; de Oliveira, Anna Kézia Soares; Oliveira, Pablo Henrique de Almeida; da Silva, Antonio Gideilson Correia; Jardim, Alexandre Maniçoba da Rosa Ferraz [UNESP]; Silva, Daniel Valadão; Barros Júnior, Aurélio Paes; da Silveira, Lindomar Maria (FapUNIFESP (SciELO), 2025)
Using non-destructive and low-cost methods to determine leaf area has gained important applications. The research objectives were (i) to propose a non-destructive method to estimate the leaf area of castor bean crops and (ii) to build equations that accurately and quickly estimate the leaf area of specie. One thousand healthy and expanded leaves of five castor bean cultivars (New Zealand Purple, Sipeal, Carmencita, Amarelo de Irecê, and IAC-80) were collected, and 200 leaves were collected from each. The maximum length, maximum width, and leaf area were calculated for each leaf. The product between length and width (LW) were calculated. We performed tests with different linear and non-linear regression models between leaf area and linear leaf dimensions of each cultivar. The models used were linear, linear without intercept, and power. The criteria for choosing the best models to estimate the leaf area of ​castor beans were a higher coefficient of determination, more elevated Pearson’s linear correlation coefficient, lower Akaike information criterion, higher Willmott agreement index, and smallest root mean square error. The equations that presented the best criteria for estimating the leaf area of ​castor bean cultivars were those that used the product between length and width, compared to equations that used only one leaf dimension. The model ŷ = 0.439 × LW can be used to accurately and quickly estimate the castor bean leaf area through linear measurements of the leaves, using the product between length and width (LW), regardless of the cultivar chosen.
Equações alométricas para predição da área foliar de cultivares de mamona
Keywords: biometrics; empirical modeling; linear dimensions; Ricinus communis L