A comparison of diameter distribution models for<i>Khaya ivorensis</i>A.Chev. plantations in Brazil

Mayrinck, Rafaella Carvalho; Filho, Antônio Carlos Ferraz; Ribeiro, Andressa; Oliveira, Ximena Mendes de; Lima, Renato Ribeiro de

doi:10.2989/20702620.2018.1463189

Cited by 10 publications

(3 citation statements)

References 19 publications

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“…Scolforo et al [38] also found the methods of Moments and MLE as the preferred fitting methods for Johnson's SB in describing the diameter distribution in Loblolly pine. Johnson's SB and Weibull have been used consistently in quantitative forestry studies because of their flexibility to describe different shapes (e.g., [6,12,20,[39][40][41]). In addition to the derivative method by Moments, the Weibull and Johnson's SB functions can be fitted by other derivative methods, for example, Weibull by percentiles [42], Johnson's SB by mode [43], conditional maximum likelihood (CML) [44], linear regression [39], percentiles [45], etc.…”

Section: Discussionmentioning

confidence: 99%

A Comparison of Probability Density Functions Fitted by Moments and Maximum Likelihood Estimation Methods Used for Diameter Distribution Estimation

Gorgoso-Varela,

Adedapo,

Ogana

2024

Forests

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Modeling diameter distribution is a crucial aspect of forest management, requiring the selection of an appropriate probability density function or cumulative distribution function along with a fitting method. This study compared the suitability of eight probability density functions—A Charlier, beta, generalized beta, gamma, Gumbel, Johnson’s SB, and Weibull (two- and three-parameter)—fitted using both derivative methods (Moments) fitted in SAS/STATTM and optimization methods (MLE) fitted with the ‘optim’ function in R for diameter distribution estimation in forest stands. The A Charlier and Gumbel functions were used for the first time in this type of comparison. The data were derived from 167 permanent sample plots in an Atlantic forest (Quercus robur) and 59 temporary sample plots in tropical forests (Tectona grandis). Fit quality was assessed using various indices, including Kolmogorov–Smirnov, Cramér–von Mises, mean absolute error, bias, and mean squared error. The results indicated that Johnson’s SB function was more suitable for describing the diameter distribution of the stands. Johnson’s SB, three-parameter Weibull, and generalized beta consistently performed well across different fitting methods, while the fits produced by gamma, Gumbel, and two-parameter Weibull were of poor quality.

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Section: Discussionmentioning

confidence: 99%

A Comparison of Probability Density Functions Fitted by Moments and Maximum Likelihood Estimation Methods Used for Diameter Distribution Estimation

Gorgoso-Varela,

Adedapo,

Ogana

2024

Forests

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“…We evaluate that most of the research that was published citing K. ivorensis from Brazilian plantations prior to 2019 were related to K. grandifoliola. The following works we are certain that studied K. grandifoliola, and not K. ivorensis as reported: Ribeiro et al (2016;2017;2018a;2018b); Mayrinck et al (2018); Oliveira et al (2018). While we are fairly sure -given the location mentioned on the Materials -of other research papers that report findings of K. grandifoliola and not K. ivorensis (e.g.…”

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confidence: 82%

African Mahogany Plantation Highlights in Brazil

et al. 2021

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African mahogany is the common name of species from the Khaya genus and yields high value timber. It is planted in monocultures and agrosilvipastoral systems in Brazil since the 90's. Here we relate the taxonomic identification of the most planted African mahogany species in Brazil, changing from Khaya ivorensis A. Chev. to K. grandifoliola C. DC. Currently we estimate there is circa 50 thousand hectares of plantations in Brazil, half concentrated in the Southeast region, with the most planted species K. grandifoliola, followed by K. senegalensis (Desr.) A. Juss.

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“…At present, a variety of probability density distribution functions have been used to describe the stand diameter structure, such as normal distribution, lognormal distribution, beta distribution [4], Johnson's SB distribution [5], distribution and Weibull distribution [2,[6][7][8][9][10][11][12][13]. These distribution functions have demonstrated their respective advantages under different regional conditions and tree types, among which the Weibull distribution function was characterized by great adaptability and flexibility, simple parameter estimation and obvious biological significance of parameters, and has been widely used in the study of stand diameter distribution modelling [14][15][16][17][18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Stand Diameter Distribution Modeling and Prediction Based on Maximum Entropy Principle

Chen

Min

2019

Forests

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Research Highlights: Improving the prediction accuracy represents a popular forest simulation modeling issue, and exploring the optimal maximum entropy (MaxEnt) distribution is a new effective method for improving the diameter distribution model simulation precision to overcome the disadvantages of Weibull. Background and Objectives: The MaxEnt distribution is the closest to the actual distribution under the constraints, which are the main probability density distributions. However, relatively few studies have addressed the optimization of stand diameter distribution based on MaxEnt distribution. The objective of this study was to introduce application of the MaxEnt distribution on modeling and prediction of stand diameter distribution. Materials and Methods: The long-term repeated measurement data sets consisted of 260 diameter frequency distributions from China fir (Cunninghamia lanceolate (Lamb.) Hook) plantations in the southern China Guizhou. The Weibull distribution and the MaxEnt distribution were applied to the fitting of stand diameter distribution, and the modeling and prediction characteristics of Weibull distribution and MaxEnt distribution to stand diameter distribution were compared. Results: Three main conclusions were obtained: (1) MaxEnt distribution presented a more accurate simulation than three-parametric Weibull function; (2) the Chi-square test showed diameter distributions of unknown stands can be well estimated by applying MaxEnt distribution based on the plot similarity index method (PSIM) and Weibull distribution based on the parameter prediction method (PPM); (3) the MaxEnt model can deal with the complex nonlinear relationship and show strong prediction ability when predicting the stand distribution structure. Conclusions: With the increase of sample size, the PSIM has great application prospects in the dynamic prediction system of stand diameter distribution.

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A comparison of diameter distribution models forKhaya ivorensisA.Chev. plantations in Brazil

Cited by 10 publications

References 19 publications

A Comparison of Probability Density Functions Fitted by Moments and Maximum Likelihood Estimation Methods Used for Diameter Distribution Estimation

A Comparison of Probability Density Functions Fitted by Moments and Maximum Likelihood Estimation Methods Used for Diameter Distribution Estimation

African Mahogany Plantation Highlights in Brazil

Stand Diameter Distribution Modeling and Prediction Based on Maximum Entropy Principle

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