Volumetria de Genipa americana em plantio homogêneo no Sudoeste da Bahia

Rocha, Mislene Barbosa; Barreto-Garcia, Patrícia Anjos Bittencourt; Prado, Wilmerson Bernardino; Paula, Alessandro de; Júnior, Valdemiro Conceição

doi:10.4336/2015.pfb.35.84.862

Cited by 4 publications

(5 citation statements)

References 5 publications

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“…Therefore, the lower the values of these two statistics, the more accurate and reliable the adjusted equation is (Schneider et al, 2009). According to Rocha et al (2015), AIC admits the existence of a real model that describes unknown data, which provides a relative measure of the information lost when a certain model is used to describe reality. It can be inferred that when the sum of the error squared increases in the regression variance analysis, AIC also increases; this justifies not selecting models ( 6) and ( 7) (Table 3), which both presented excellent values of R²aj and F value of ANOVA but also high AIC.…”

Section: Discussionmentioning

confidence: 99%

Modeling the leaf area of Ormosia paraensis Ducke by statistical models and artificial neural networks

Santos

Soares

Pimentel

et al. 2018

Chil. j. agric. res.

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The leaf is a very important plant structure because it allows gas exchange and transformation of light energy into chemical energy. The objective of this research study was to test artificial neural networks (ANNs) to estimate the leaf area (LA) of Ormosia paraensis Ducke and compare their performance with adjusted statistical models. One hundred forty leaves were selected from seedlings of the species at the leaf age of 100 days after sowing (DAT). The LA was calculated by indirect estimation with ImageJ software, and the linear length (L) and width (W) dimensions were measured with a ruler graduated in centimeters. Afterward, 90 leaves were randomly separated to generate mathematical equations of LA (Y) as a function of linear dimensions (L and W), and 50 leaves to validate the equations. Similarly, 100 networks of the multilayer perceptron (MLP) type were trained with the backpropagation algorithm and the best networks were selected for later validation. The choice of the best equations and ANNs was based on precision and dispersion statistics. The quadratic equation obtained from model (4) demonstrated consistent statistics with the coefficient of determination (R²aj = 87.39) and a low result for the standard error of estimate (SEE = 12.07%). For the ANNs, a correlation coefficient (Ryŷ), varying between 0.9316 and 0.9521, was obtained in the training phase, while it ranged from 0.8522 to 0.8825 in the validation phase, generating lower residues. It is concluded that the ANN performance was higher compared with the conventional regression technique.

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Section: Discussionmentioning

confidence: 99%

Modeling the leaf area of Ormosia paraensis Ducke by statistical models and artificial neural networks

Santos

Soares

Pimentel

et al. 2018

Chil. j. agric. res.

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“…The Shumacher and Hall model exhibited the best fit for Mimosa caesalpinifolia in relation to the other models, which recorded lower R 2 values and RMSE close to 32%. Melo et al (2013), Rocha et al (2015), and Silva et al (2016) used models to estimate volume in homogeneous stands of Genipa americana, Khaya ivorensis, and Pinus caribaea, respectively, and concluded that the Spurr model showed the best fit, with little dispersion between estimated and observed values. The results found in the present study for Genipa americana and Anadantera macrocarpa are similar to those obtained in the aforementioned investigations.…”

Section: Resultsmentioning

confidence: 99%

“…Five linear regression models were used to analyze volume in the species studied, as described by Soares et al (2011); Rocha et al (2015); Silva et al (2016). Timber volume was estimated based on the DBH and plant height, as shown in Table 1.…”

Section: Methodsmentioning

confidence: 99%

Artificial neural networks and regression analysis for volume estimation in native species

Amorim

Leite

Souza

et al. 2021

Rev. bras. eng. agríc. ambient.

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Modeling is an important tool to estimate forest production in planted areas. Although this issue has been studied worldwide, knowledge regarding volume measurement in specific locations such as Northeast Brazil is still scarce. The present study aimed to evaluated the effectiveness of artificial neural networks (ANNs) and regression analysis in estimating the timber volume of homogeneous stands of Anadantera macrocarpa, Genipa americana, and Mimosa casalpinifolia, in order to better predict the growth and production of these species. Both methods were suitable for estimating the individual volume in 7-year-old stands with different spacing. The Spurr regression model showed better statistical results and dispersion of unbiased errors for Anadantera macrocarpa and Genipa americana, whereas the Shumacher-Hall model provided more accurate volume estimates for Mimosa caesalpinifolia. The ANNs calibrated with two neurons in the middle layer exhibited the best fit for all three species. As such, artificial neural networks can be recommended to estimate the individual volumes of the species analyzed in the study area.

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“…Figure 5 shows photographs of fruit pulps marketed by a company in Alagoas State. In addition to occurring naturally in Brazilian ecosystems, these three species are also domestically grown and cultivated on a small/medium scale (Araújo et al, 2010;Mattietto and Matta, 2011;Rocha et al, 2015), contributing to the reduction of conservation pressures.…”

Section: Strategic Species For Food and Nutrition Security And Biocul...mentioning

confidence: 99%

Wild food plants with the potential to improve food and nutrition security may be threatened by timber extraction: A systematic review of the Brazilian context

Caetano,

Monique da Costa Santos,

Zago Poian

et al. 2023

Ethnobio Conserv

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Wild food plants can contribute to improving the food and nutrition security of local populations by promoting diet diversification and increasing the intake of micro- and macronutrients. However, many of these plants are also used as timber. Wild food species need to be identified and their food–wood use interactions need to be well understood for the development of conservation strategies, as species with the potential to improve food and nutrition security may be threatened by destructive extraction. This systematic review recorded and compiled nutritional information on woody plant species native to Brazil that are used by local populations for food and timber purposes, seeking to identify which species have a high overlap between food and timber uses as well as a high nutritional potential. A total of 635 woody species with timber and/or food uses were identified. Of this total, at least 42 species find application in all timber use categories analyzed in this study, being considered versatile. Comparison of ethnobiological and nutritional data revealed nine versatile species for which nutritional composition information was available, among which three stood out in terms of macronutrient contents, namely Anacardium occidentale L., Bauhinia cheilantha (Bong.) Steud., and Eugenia pyriformis Cambess. Many versatile species classified as threatened or in decline have not been the focus of nutritional studies, which signals the need for greater nutritional research efforts. It is also necessary to investigate whether food importance exerts any protective effect on these species, reducing timber use pressure (protection hypothesis).

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Volumetria de Genipa americana em plantio homogêneo no Sudoeste da Bahia

Cited by 4 publications

References 5 publications

Modeling the leaf area of Ormosia paraensis Ducke by statistical models and artificial neural networks

Modeling the leaf area of Ormosia paraensis Ducke by statistical models and artificial neural networks

Artificial neural networks and regression analysis for volume estimation in native species

Wild food plants with the potential to improve food and nutrition security may be threatened by timber extraction: A systematic review of the Brazilian context

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