Leaf area estimation in triticale by leaf dimensions

Toebe, Marcos; Melo, Patrícia Jesus de; Souza, Renato Santos de; Mello, Anderson Chuquel; Tartaglia, Francieli de Lima

doi:10.5039/agraria.v14i2a5656

Cited by 7 publications

(11 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Minimum, maximum, mean, total amplitude, median, variance, standard deviation, standard error, and coefficient of variation for length (L), width (W), product of length by width (L.W), and real leaf area (LA) of 200 Psychotria colorata leaf blades. Scatterplots between L, W, L.W, and LA showed different dispersion patterns (Figure 3), indicating that the data fit linear and non-linear regression models, as found in other similar studies (Toebe et al 2019;Ribeiro et al 2020b). Figure 4 shows the leaf area percentage distribution by different intervals.…”

Section: Resultssupporting

confidence: 79%

Allometric equations to estimate the leaf area of Psychotria colorata (Willd. Ex Schult.) Müll.Arg.

et al. 2021

View full text Add to dashboard Cite

Estimating leaf area using non-destructive methods from regression equations has become a more efficient, quick, and accurate way. Thus, this study aimed to propose an equation that significantly estimates the leaf area of Psychotria colorata (Rubiaceae) through linear leaf dimensions. For this purpose, 200 leaves of different shapes were collected, and length (L), width (W), product of length by width (L.W), and real leaf area (LA) of each leaf blade were determined. Then, equations were adjusted for predicting leaf area using simple linear, linear (0.0), quadratic, cubic, power, and exponential regression models. The proposed equation was selected according to the coefficient of determination (R²), Willmott's agreement index (d), Akaike's information criterion (AIC), mean absolute error (MAE), mean squared error (RMSE) and BIAS index. It was noted that the equations adjusted using L.W met the best criteria for estimating leaf area, but the equation LA = 0.59 * L.W from linear regression without intercept was the most suitable. This equation predicts that 59% of leaf area is explained by L.W. Concluding, the leaf area of P. colorata can be estimated using an allometric equation that uses linear leaf blade dimensions.

show abstract

Section: Resultssupporting

confidence: 79%

Allometric equations to estimate the leaf area of Psychotria colorata (Willd. Ex Schult.) Müll.Arg.

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Among the 1,452 leaves used for generation and among the 363 leaves used for validation of the models, the CV of the length x width product (LW) of the leaf blade and leaf area (Y) was approximately twice the CV of the length (L) and width (W) of the leaf blade (Table 2). A similar pattern was observed in leaves of coccoloba (Mariano et al, 2009), snap bean (Toebe et al, 2012), forage turnip (Cargnelutti et al, 2012), pigeon pea (Cargnelutti et al, 2015), yacon (Cunya et al, 2017), sunn hemp (Carvalho et al, 2017), dwarf pigeon pea (Pezzini et al, 2018), triticale (Toebe et al, 2019), and coffee (Cavallaro et al, 2020).…”

Section: Resultssupporting

confidence: 61%

“…In the scatter plots, it is observed that there are patterns of nonlinearity between L and Y and between W and Y and linearity between LW and Y, which suggests better fit of nonlinear and linear models, respectively (Figure 2). These patterns have also been verified in forage turnip (Cargnelutti et al, 2012), pigeon pea (Cargnelutti et al, 2015), sunn hemp (Carvalho et al, 2017), and triticale (Toebe et al, 2019).…”

Section: Resultsmentioning

confidence: 56%

“…Alternatively, if the researcher wants to minimize the work and make only one measurement on the leaves, he/she should opt for W and for quadratic (Ŷ = -0.2884 + 1.1428x + 0.5832x 2 , R 2 = 0.9262) or power (Ŷ = 1.1536x 1.7752 , R 2 = 0.9260) models. However, these two models have a poorer fit when compared to the models generated from the LW (two measurements), but a better fit when compared to those generated from L. Models generated based on LW were recommended to estimate leaf area in species such as: coccoloba (Mariano et al, 2009), sunn hemp (Cardozo et al, 2011;Carvalho et al, 2017), snap beans (Lakitan et al, 2017), forage turnip (Cargnelutti et al, 2012), pigeon pea (Cargnelutti et al, 2015), yacon (Cunya et al, 2017), dwarf pigeon pea (Pezzini et al, 2018), triticale (Toebe et al, 2019), and coffee (Cavallaro et al, 2020 buckwheat, in which the linear model without intercept was also the most appropriate for estimating the leaf area of Coccoloba rosea (Ŷ = 0.7705x, R =0.98) and Coccoloba ramosissima (Ŷ = 0.7416x, R 2 =0.91), as a function of LW (x) (Mariano et al, 2009). For buckwheat, leaf area models were generated for the variety Hruszowska (Almehemdi et al, 2017), but with a methodology different from that used in the present study.…”

Section: Resultsmentioning

confidence: 99%

“…Precise models for leaf area estimation as a function of the linear dimensions of the leaves have been generated for species of the same family as buckwheat, such as coccoloba (Mariano, Amorim, Mariano, & Silva, 2009), in soil cover species, such as: sunn hemp (Cardozo, Parreira, Amaral, Alves, & Bianco, 2011;Carvalho, Toebe, Tartaglia, Bandeira, & Tambara, 2017), forage turnip (Cargnelutti, Toebe, Burin, Fick, & Casarotto, 2012), pigeon pea (Cargnelutti, Toebe, Alves, & Burin, 2015), and dwarf pigeon pea (Pezzini et al, 2018); and in agricultural species such as: snap beans (Lakitan, Widuri, & Meihana, 2017;, yacon (Cunya, Edquén, & Zumaeta, 2017), triticale (Toebe, Melo, Souza, Mello, & Tartaglia, 2019), and coffee (Cavallaro, Uber-Bucekb, & Finzer, 2020).…”

Section: Modelagem Trigo Sarracenomentioning

confidence: 99%

See 2 more Smart Citations

Estimation of buckwheat leaf area by leaf dimensions

Filho¹,

Pezzini²,

Neu³

et al. 2021

Semina: Ciênc. Agrár.

View full text Add to dashboard Cite

The objective of this work was to model and identify the best models for estimating the leaf area, determined by digital photos, of buckwheat (Fagopyrum esculentum Moench) of the cultivars IPR91-Baili and IPR92-Altar, as a function of length (L), width (W) or length x width product (LW) of the leaf blade. Ten uniformity trials (blank experiments) were carried out, five with IPR91-Baili cultivar and five with IPR92-Altar cultivar. The trials were performed on five sowing dates. In each trial and cultivar, expanded leaves were collected at random from the lower, middle and upper segments of the plants, totaling 1,815 leaves. In these 1,815 leaves, L and W were measured and the LW of the leaf blade was calculated, which were used as independent variables in the model. The leaf area of each leaf was determined using the digital photo method (Y), which was used as a dependent variable of the model. For each sowing date, cultivar and thirds of the plant, 80% of the leaves (1,452 leaves) were randomly separated for the generation of the models and 20% of the leaves (363 leaves) for the validation of the models of leaf area estimation as a function of linear dimensions. For buckwheat, IPR91-Baili and IPR92-Altar cultivars, the quadratic model (Ŷ = 0.5217 + 0.6581LW + 0.0004LW2, R2 = 0.9590), power model (Ŷ = 0.6809LW1.0037, R2 = 0.9587), linear model (Ŷ = 0.0653 + 0.6892LW, R2 = 0.9587) and linear model without intercept (Ŷ = 0.6907LW, R2 = 0.9587) are indicated for the estimation of leaf area determined by digital photos (Y) based on the LW of the leaf blade (x), and, preferably, the linear model without intercept can be used, due to its greater simplicity.

show abstract

Leaf Area Estimation of Cenostigma pyramidale: A Native Brazilian Caatinga Species

Pompelli,

Jarma-Orozco,

Jaraba-Navas

et al. 2024

International Journal of Forestry Research

View full text Add to dashboard Cite

Global climatic changes are shifting, leading to increased temperatures and unpredictable rainfall changes. Tropical biomes, including the Caatinga in Northeast Brazil, face the risk of aridification. The Caatinga, covering 10.7% of Brazil, is an area that is highly susceptible to desertification, with a projected 5°C temperature rise. The region’s unique biodiversity faces threats from human activities, including deforestation and habitat fragmentation. Cenostigma pyramidale is a Brazilian native species, very common in the Caatinga ecosystem. The plant is vital for its adaptability to dry season and seasonality and presents a potential for use in reforestation programs. However, it also faces challenges like deforestation and habitat fragmentation. This study aims to estimate the leaf area of C. pyramidale using nondestructive and cost‐effective methods. The proposed model, based on leaf length and width, provides reliable and accurate leaf area (LA) estimates. Then, this study recommends the equation LA = 0.746 × (LW)0.979 as the best for estimating leaf area due to its high accuracy and biological consistency. The equation LA = 0.725 × (LW) is recommended as a simpler equation because the linear equation is easier to do mainly in land cases or in more extensive experiments. The equation LA = 0.645 × (L)1.652 eliminates 50% of the allometric process because only one leaf dimension is used to estimate leaf area. The leaf area estimation model provides a practical tool for researchers studying plant physiology and agronomic decision‐making. The findings emphasize the importance of understanding the impact of climate change on species distribution, especially in vulnerable regions like the Caatinga.

show abstract

Leaf area estimation in triticale by leaf dimensions

Cited by 7 publications

References 19 publications

Allometric equations to estimate the leaf area of Psychotria colorata (Willd. Ex Schult.) Müll.Arg.

Allometric equations to estimate the leaf area of Psychotria colorata (Willd. Ex Schult.) Müll.Arg.

Estimation of buckwheat leaf area by leaf dimensions

Leaf Area Estimation of Cenostigma pyramidale: A Native Brazilian Caatinga Species

Contact Info

Product

Resources

About