Analysis of Cultivar-Specific Variability in Size-Related Leaf Traits and Modeling of Single Leaf Area in Three Medicinal and Aromatic Plants: Ocimum basilicum L., Mentha Spp., and Salvia Spp.

Abstract

In this study, five allometric models were used to estimate the single leaf area of three well-known medicinal and aromatic plants (MAPs) species, namely basil (Ocimum basilicum L.), mint (Mentha spp.), and sage (Salvia spp.). MAPs world production is expected to rise up to 5 trillion US$ by 2050 and, therefore, there is a high interest in developing research related to this horticultural sector. Calibration of the models was obtained separately for three selected species by analyzing (a) the cultivar variability-i.e., 5 cultivars of basil (1094 leaves), 4 of mint (901 leaves), and 5 of sage (1103 leaves)-in the main two traits related to leaf size (leaf length, L, and leaf width, W) and (b) the relationship between these traits and single leaf area (LA). Validation of the chosen models was obtained for each species using an independent dataset, i.e., 487, 441, and 418 leaves, respectively, for basil (cv. 'Lettuce Leaf'), mint (cv. 'Comune'), and sage (cv. 'Comune'). Model calibration based on fast-track methodologies, such as those using one measured parameter (one-regressor models: L, W, L 2 , and W 2 ) or on more accurate two-regressors models (L × W), allowed to achieve different levels of accuracy. This approach highlighted the importance of considering intra-specific variability before applying any models to a certain cultivar to predict single LA. Eventually, during the validation phase, although modeling of single LA based on W 2 showed a good fitting (R 2 basil = 0.948; R 2 mint = 0.963; R 2 sage = 0.925), the distribution of the residuals was always unsatisfactory. On the other hand, two-regressor models (based on the product L × W) provided the best fitting and accuracy for basil (R 2 = 0.992; RMSE = 0.327 cm 2 ), mint (R 2 = 0.998; RMSE = 0.222 cm 2 ), and sage (R 2 = 0.998; RMSE = 0.426 cm 2 ).Plants 2020, 9, 13 2 of 21 contribute towards food security and social stability. Within this scope, applied research on innovative horticultural practices can make effective use of dynamic crop growth models [4] under conditions optimal for plant growth and for eliciting plant response to abiotic stresses [5], therefore, allowing a more rational use of resources, such as water and nutrients [4].Several fundamental physiological processes such as photosynthesis, transpiration, and cooling are facilitated by leaves [6] and they are, therefore, strongly influenced by leaf morphology (size, shape, symmetry, venation, organization, and petiole characteristics) [7]. The characterization of leaf morphology and quantification of leaf area (LA) and/or leaf area index (LAI) is consequently of paramount importance to horticultural crop science. In this respect, there is an increasing interest in using computer-assisted imaging systems [8] for producing reliable biometric measurements [9] and analyzing phenotypic traits related to plant architecture and leaf characteristics [10]. For instance, data on leaf characteristics can be incorporated into databases [11,12] and employed to validate time-series quantification of lea...

Allometric models for estimating Moringa oleifera leaflets area

“…The significant results associated with the high coefficients of determination indicate that the fitted models can be used to estimate the leaflet area of M. oleifera, and the model that relates the product between leaflet length and width is the most indicated for this purpose. These results are consistent with those obtained by Morgado et al (2013) in species of passion fruit (Passiflora alata, P. coccinea, P. gibertii, P. ligularis, P. misera, P. mucronata, P. nitida, P. setacea), by Wang et al (2019) in broadleaf species, by Oliveira et al (2019) in 'caruru' (Talinum triangulare) and 'beldroega grande' (Talinum paniculatum), by Hara et al (2019) in common beans (Phaseolus vulgaris) and by Teobaldelli et al (2020b) when fitting allometric models in the estimation of the leaf area of Ocimum basilicum L., Mentha Spp., and Salvia Spp. When the precision of the model was evaluated through Pearson's correlation coefficient "r", it was possible to note that the models ELA = β 0 + β 1 *LW and ELA = β 0 + β 1 *L + β 2 *W had the best performance, with values higher than 0.98.…”

“…Several fundamental physiological processes, such as photosynthesis and transpiration are influenced by leaf morphology, so the morphometric characterization of leaflets and modeling of leaflets area are important tools for estimating crop yield in agroecosystems (Teobaldelli et al, 2020a). Measurements of length and width and the product of these dimensions can be used to estimate leaf area with non-destructive methods (Teobaldelli et al, 2020b).…”

Scaling the leaf length-times-width equation to predict total leaf area of shoots

“…First, we focused on individual leaves. Within a species, the area of an individual leaf ( A leaf ) is proportional to the lamina length ( L leaf ) times lamina width ( W leaf ) ( Cain and Castro, 1959 ; Teobaldelli et al ., 2019 a , b ; Yu et al , 2020 ; Huang et al , 2021 ; Li et al , 2021 ; Schrader et al , 2021 ; Shi et al , 2021 a ) ( Fig. 2A ):…”

MÉTODO NÃO DESTRUTIVO PARA DETERMINAÇÃO DA ÁREA FOLIAR DE Turnera subulata

“…O uso de equações matemáticas para estimar a área foliar já foi relatada na literatura para diversas espécies (POMPELLI et al, 2012;SCHMILDT et al, 2015;MONTOLATTO et al, 2020;OLIVEIRA et al, 2017;OLIVEIRA et al, 2019;OLIVEIRA et al, 2020;SANTOS et al, 2020;TEOBALDELLI et al, 2020), entretanto, não se encontrou estudos que envolvam esta técnica para estimar a área foliar de T. subulata.…”

“…Deve-se ressaltar, que o presente estudo foi realizado com plantas em condição de campo e com folhas sadias, sem o ataque de pragas ou doenças. Assim, caso se faça necessário o uso desta equação em outras condições diferente das deste estudo, essa deve ser validada sob tais condições para melhores estimativas (TEOBALDELLI et al, 2020). Figura 2.…”