Productivity estimate using regression and artificial neural networks in small familiar areas with agrosilvopastoral systems

Silva, Simone; Neto, Sílvio Nolasco de Oliveira; Leite, Hélio Garcia; Alcântara, Aline Edwiges Mazon de; Neto, Ricardo Rodrigues de Oliveira; Souza, Guilherme Silvério Aquino de

doi:10.1007/s10457-020-00526-1

Cited by 10 publications

(5 citation statements)

References 65 publications

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“…and Swietenia macrophylla King in Hook in wide-spacing arrangements (Fernandes et al, 2017), and African mahogany species established in AGF systems (Santos et al, 2018). The Schumacher-Hall model was also chosen by Paula et al (2013), Silva et al (2020), andCerqueira et al (2020) to fit volume equations for eucalypt trees established at different spacings in AGF systems, with good fitting statistics.…”

Section: Resultsmentioning

confidence: 99%

Eucalypt clone modelling in agrosilvopastoral systems

Júnior¹,

Neto²,

Soares³

et al. 2022

Pesq. agropec. bras.

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The objective of this work was to evaluate height-diameter, volumetric, and taper models to estimate the height, volume, and bole profile of trees of eucalypt clones (Eucalyptus grandis × Eucalyptus urophylla) in an agrosilvopastoral system. Data were collected from permanent plots in an eight year-old agrosilvopastoral system, composed by three eucalypt clones (VE01, VE06, and VE07), located in the municipality of Coronel Xavier Chaves, in the state of Minas Gerais, Brazil. Two height-diameter, three volumetric, and four taper models were fit to the data of each clone and compared to each other, in order to select the best-fitting one. The equations fitted well to the observed data, and those of the models of Campos, Schumacher-Hall, and Garay stood out as the best ones. In addition, Graybill's F-test showed that the height-diameter and volumetric equations must be fitted separately for each genetic material. The model of Garay was the best taper model to estimate the bole profiles of all clones using a single equation.

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Section: Resultsmentioning

confidence: 99%

Eucalypt clone modelling in agrosilvopastoral systems

Júnior¹,

Neto²,

Soares³

et al. 2022

Pesq. agropec. bras.

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“…The purpose of silvopastoral systems adoption in small or medium family-based farms is to diversify income through timber production (de Sousa et al 2016;Silva et al 2020;Caldwell et al 2021).…”

Section: Discussionmentioning

confidence: 99%

“…Indeed, some efforts have been made to develop volumetric equations for trees in these systems (Abrantes et al 2019;Lemos-Junior et al 2016;Silva et al 2020;Aguiar Júnior et al 2022). However, uncertainty about the best models persists due to scatter in assessment methods, models tested, biomes, species, and spatial arrangements, as noted by Aguiar Júnior et al (2022).…”

Section: Introductionmentioning

confidence: 99%

“…New approaches, such as artificial neural networks (ANNs) and nonlinear mixed modeling, have been highlighted by some authors (Schikowski et al 2018;Sanqueta et al 2018;Silva et al 2020;Dolácio et al 2020;Lopes et al 2020) as the best alternatives to fit volumetric equations. Conversely, these methods require specialization and the use of more expensive methodological tools.…”

Section: Introductionmentioning

confidence: 99%

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Tree volume estimation for small silvopastoral systems: do we really need the height variable to obtain sufficiently accurate results?

Müller

Borges

Resende

et al. 2022

Preprint

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The objective of this study was to evaluate and compare the performance of single and double-entry volumetric models in estimating tree volume of eucalypt trees in different silvopastoral systems in Coronel Pacheco, Minas Gerais State, Brazil. Diameter at breast height, total height, and diameter outside the bark along the stems of the sample trees were recorded. Four single-entry and five double-entry volume models were fitted to the observed data for six strata consisting of different Eucalyptus genetic material and three silvopastoral systems, and were compared to select the best alternative. Double-entry models, specifically logarithmic Spurr and logarithmic Schumacher & Hall, fitted statistically better then single-entry ones for all but one of the strata, where they were overcome by the Husch’s model. However, although the superiority of the former can have been easily verified by different and complementary statistics, we found that the volume estimates provided by the best double-entry and the best single-entry model of each stratum differed by a quantity that can be considered irrisory, from both practical and monetary points of view. In a per tree basis, the differences, in absolute values, did not surpassed 0.051 m3, or only US$ 0.25, considering a market value of US$ 4.90/m3. And even when simulating a real scenario of batch sales for each stratum, by simple extrapolation of the mean volume and considering their effective tree stand, such differences did not exceed ±0.25 m3/ha, or ±US$ 1.20/ha. These findings suggest that the smallholder farmer does not need to estimate the height of its trees; more than this, he/she can save money by not having to do a complete forest inventory and by using single-entry models such as the Husch model for estimating the wood volume of his/her plant stand. Another highlight or reinforcement of this work is that the use of the Furnival index was crucial for a reliable selection of the best models, once it allows for comparisons at the same scale of variation.

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“…Utilizou-se o algoritmo Resilient Propagation na sua variação RPROP+ por ser um algoritmo eficiente em trabalhos recentes na área florestal (Tavares-Júnior et al, 2019;Freitas et al, 2020;Silva et al, 2020).…”

Section: Materiais E Métodosunclassified

Estimativas volumétricas em povoamentos de eucalipto utilizando máquinas de vetores de suporte e redes neurais artificiais

Cordeiro¹,

Arce²,

Retslaff³

et al. 2022

MYB

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Este estudo teve por objetivo avaliar o desempenho de redes neurais artificiais (RNA) e máquinas de vetor de suporte (MVS) na modelagem volumétrica em povoamentos de eucalipto. Utilizou-se dados oriundos de plantios comerciais não desbastados, localizados em quatro municípios na mesorregião sul do estado do Amapá e foram disponibilizados por uma empresa privada. Foram ajustados modelos volumétricos consagrados na literatura e comparados com a técnica de MVS e de RNA. Os dados foram divididos em 80% para treinamento e 20% para validação dos modelos, as mesmas variáveis dendrométricas utilizadas pelos modelos de regressão (dap e altura) foram utilizadas pelas MVS e RNA. Para o treinamento e generalização das MVS, foram utilizadas quatro configurações, formadas a partir de duas funções de erro e duas funções de Kernel. Para configuração, treinamento e generalização das RNA, foi utilizado o software NeuroForest -Volumetric, no qual foram utilizadas configurações de redes do tipo Adaline (Adaptive Linear Element); Multilayer Perceptron (MLP) e Funções de Base Radial (RBF). A qualidade dos ajustes dos modelos de regressão, e das metodologias utilizando RNA e MVS, foram avaliadas utilizando-se o coeficiente de correlação entre os volumes individuais observados e estimados (ryŷ), a raiz quadrada do erro médio, expresso em porcentagem da média (RMSE%), análise gráfica dos resíduos (Res%). Considerando os resultados, MVS e RNA obtiveram desempenho ligeiramente melhores, comparados à metodologia tradicional, nas estimativas de volume individual, demonstrando serem técnicas que se adequaram bem para aplicações na área de mensuração e manejo florestal. PALAVRAS CHAVE: análise de regressão, aprendizado de máquina, volumetria.

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Productivity estimate using regression and artificial neural networks in small familiar areas with agrosilvopastoral systems

Cited by 10 publications

References 65 publications

Eucalypt clone modelling in agrosilvopastoral systems

Eucalypt clone modelling in agrosilvopastoral systems

Tree volume estimation for small silvopastoral systems: do we really need the height variable to obtain sufficiently accurate results?

Estimativas volumétricas em povoamentos de eucalipto utilizando máquinas de vetores de suporte e redes neurais artificiais

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