Home / Regular Issue / JSSH Vol. 30 (2) Apr. 2022 / JST-3072-2021

 

Improving Yield Projections from Early Ages in Eucalypt Plantations with the Clutter Model and Artificial Neural Networks

Gianmarco Goycochea Casas, Leonardo Pereira Fardin, Simone Silva, Ricardo Rodrigues de Oliveira Neto, Daniel Henrique Breda Binoti, Rodrigo Vieira Leite, Carlos Alberto Ramos Domiciano, Lucas Sérgio de Sousa Lopes, Jovane Pereira da Cruz, Thaynara Lopes dos Reis and Hélio Garcia Leite

Pertanika Journal of Social Science and Humanities, Volume 30, Issue 2, April 2022

DOI: https://doi.org/10.47836/pjst.30.2.22

Keywords: Artificial intelligence, data structure, forest growth and yield, forest management, regression

Published on: 1 April 2022

A common issue in forest management is related to yield projection for stands at young ages. This study aimed to evaluate the Clutter model and artificial neural networks for projecting eucalypt stands production from early ages, using different data arrangements. In order to do this, the changes in the number of measurement intervals used as input in the Clutter model and artificial neural networks (ANNs) are tested. The Clutter model was fitted considering two sets of data: usual, with inventory measurements (I) paired at intervals each year (I1–I2, I2–I3, …, In–In+1); and modified, with measurements paired at all possible age intervals (I1–I2, I1–I3, …, I2–I3, I2–I4, …, In–In+1). The ANN was trained with the modified dataset plus soil type and geographic coordinates as input variables. The yield projections were made up to the final ages of 6 and 7 years from all possible initial ages (2, 3, 4, 5, or 6 years). The methods are evaluated using the relative error (RE%), bias, correlation coefficient (r), and relative root mean square error (RMSE%). The ANN was accurate in all cases, with RMSE% from 8.07 to 14.29%, while the Clutter model with the modified dataset had values from 7.95 to 23.61%. Furthermore, with ANN, the errors were evenly distributed over the initial projection ages. This study found that ANN had the best performance for stand volume projection surpassing the Clutter model regardless of the initial or final age of projection.

  • Bayat, M., Ghorbanpour, M., Zare, R., Jaafari, A., & Pham, B. T. (2019). Application of artificial neural networks for predicting tree survival and mortality in the Hyrcanian forest of Iran. Computers and Electronics in Agriculture, 164, Article 104929. https://doi.org/10.1016/j.compag.2019.104929

  • Bayat, M., Bettinger, P., Hassani, M., & Heidari, S. (2021). Ten-year estimation of Oriental beech (Fagus orientalis Lipsky) volume increment in natural forests: A comparison of an artificial neural networks model, multiple linear regression and actual increment. Forestry: An International Journal of Forest Research, 94(4), 598-609. https://doi.org/10.1093/forestry/cpab001

  • Binoti, D. H. B. (2012). Emprego de Redes Neurais Artificiais em Mensuração e Manejo Florestal [Use of Artificial Neural Networks in Measurement and Forest Management] (PhD Thesis). Universidade Federal de Viçosa, Brazil.

  • Binoti, D. H. B., Binoti, M. L. M. S., Leite, H. G., & Silva, A. (2013). Redução dos custos em inventário de povoamentos equiâneos [Reduction in inventory costs in even-aged stand]. Revista Brasileira de Ciências Agrárias - Brazilian Journal of Agricultural Sciences, 8(1), 125-129. https://doi.org/10.5039/agraria.v8i1a2209

  • Braga, A. P., Carvalho, A. P. L. F., & Ludemir, T. B. (2007). Redes neurais artificiais: Teoria e aplicações [Artificial neural networks: Theory and applications] (2nd Ed). LTC Editora.

  • Burkhart, H. E., & Tomé, M. (2012). Modeling forest trees and stands. Springer Netherlands. https://doi.org/10.1007/978-90-481-3170-9

  • Campos, B. P. F., da Silva, G. F., Binoti, D. H. B., de Mendonça, A. R., & Leite, H. G. (2017). Predição da altura total de árvores em plantios de diferentes espécies por meio de redes neurais artificiais [Estimation of total tree height in plantations of different species through artificial neural networks]. Pesquisa Florestal Brasileira, 36(88), 375-385. https://doi.org/10.4336/2016.pfb.36.88.1166

  • Campos, J. C. C., & Leite, H. G. (2017). Mensuração florestal: Perguntas e respostas (5.ed. atual. e ampl.) [Forest measurement: Questions and answers (5th Ed)]. UFV.

  • Castro, R. V. O., Araújo, R. A. A., Leite, H. G., Castro, A. F. N. M., Silva, A., Pereira, R. S., & Leal, F. A. (2016). Modelagem do crescimento e da produção de povoamentos de eucalyptus em nível de distribuição diamétrica utilizando índice de local [Modeling of growth and yield of eucalyptus stands in level of diameter distribution using site index]. Revista Árvore, 40(1), 107-116. https://doi.org/10.1590/0100-67622016000100012

  • Chiarello, F., Steiner, M. T. A., Oliveira, E. B. D., Arce, J. E., & Ferreira, J. C. (2019). Artificial neural networks applied in forest biometrics and modeling: State of the art (january/2007 to july/2018). CERNE, 25(2), 140-155. https://doi.org/10.1590/01047760201925022626

  • Clutter, J. L. (1963). Compatible growth and yield models for loblolly pine. Forest Science, 9(3), 354-371. https://doi.org/10.1093/forestscience/9.3.354

  • da Cunha Neto, E. M., Bezerra, J. C. F., de Miranda, L. C., do Mar, A. L., Vaz, M. M., da Silva Melo, M. R., & da Castro Rocha, J. E. (2019). Kozak model and artificial neural networks in eucalyptus fuser sharing estimate. Revista de Engenharia e Tecnologia, 11(3), 150-158.

  • da Rocha, S. J. S. S., Torres, C. M. M. E., Jacovine, L. A. G., Leite, H. G., Gelcer, E. M., Neves, K. M., Schettini, B. L. S., Villanova, P. H., da Silva, L. F., Reis, L. P., & Zanuncio, J. C. (2018). Artificial neural networks: Modeling tree survival and mortality in the Atlantic Forest biome in Brazil. Science of The Total Environment, 645, 655-661. https://doi.org/10.1016/j.scitotenv.2018.07.123

  • da Silva Binoti, M. L. M., Binoti, D. H. B., Leite, H. G., Garcia, S. L. R., Ferreira, M. Z., Rode, R., & da Silva, A. A. L. (2014). Redes neurais artificiais para estimação do volume de árvores [Neural networks for estimating of the volume of tree]. Revista Árvore, 38(2), 283-288. https://doi.org/10.1590/S0100-67622014000200008

  • da Silva Binoti, M. L. M., Leite, H. G., Binoti, D. H. B., & Gleriani, J. M. (2015). Prognose em nível de povoamento de clones de eucalipto empregando redes neurais artificiais [Stand-level prognosis of eucalyptus clones using artificial neural networks]. CERNE, 21(1), 97-105. https://doi.org/10.1590/01047760201521011153

  • da Silva Tavares Júnior, I., da Rocha, J. E. C., Ebling, Â. A., de Souza Chaves, A., Zanuncio, J. C., Farias, A. A., & Leite, H. G. (2019). Artificial neural networks and linear regression reduce sample intensity to predict the commercial volume of eucalyptus clones. Forests, 10(3), Article 268. https://doi.org/10.3390/f10030268

  • de Abreu Demolinari, R, Soares, C. P. B., Leite, H. G., & de Souza, A. L. (2007). Crescimento de plantios clonais de eucalipto não desbastados na região de Monte Dourado (PA) [Growth of unthinned clonal eucalyptus plantations in the region of Monte Dourado (PA)]. Revista Árvore, 31(3), 503-512. https://doi.org/10.1590/S0100-67622007000300016

  • de Alcântra, A. E. M., de Albuquerque Santos, A. C., da Silva, M. L. M., Binoti, D. H. B., Soares, C. P. B., Gleriani, J. M., & Leite, H. G. (2018). Use of artificial neural networks to assess yield projection and average production of eucalyptus stands. African Journal of Agricultural Research, 13(42), 2285-2297. https://doi.org/10.5897/AJAR2017.12942

  • de Azevedo, G. B., de Oliveira, E. K. B., Azevedo, G. D. O., Buchmann, H. M., Miguel, E. P., & Rezende, A. V. (2016). Modeling production by stand and diameter distribution in eucalyptus plantations. Scientia Forestalis, 44(110), 383-392.

  • de Oliveira, B. R., da Silva, A. A. P., Teodoro, L. P. R., de Azevedo, G. B., de Oliveira Sousa Azevedo, G. T., Baio, F. H. R., Sobrinho, R. L., da Silva Junior, C. A., & Teodoro, P. E. (2021). Eucalyptus growth recognition using machine learning methods and spectral variables. Forest Ecology and Management 497, Article 119496. https://doi.org/10.1016/j.foreco.2021.119496

  • Dias, A. N., Leite, H. G., Campos, J. C. C., Couto, L., & Carvalho, A. F. (2005). Emprego de um modelo de crescimento e produção em povoamentos desbastados de eucalipto [The use of a growth and yield model in thinned eucalypt stands]. Revista Árvore, 29(5), 731-739. https://doi.org/10.1590/S0100-67622005000500008

  • dos Santos, H. G., Jacomine, P. K. T., dos Anjos, L. H. C., de Oliveira, V. A., Lumbreras, J. F., Coelho, M. R., de Almeida, J. A., de Araujo Filho, J. C., de Oliveira, J. B., & Cunha, T. J. F. (2018). Sistema brasileiro de classificação de solos (5a̲ edição revista e ampliada) [Brazilian soil classification system (5th edition revised and expanded)]. Embrapa.

  • Freitas, E. F. S., Paiva, H. N., Neves, J. C. L., Marcatti, G. E., & Leite, H. G. (2020). Modeling of eucalyptus productivity with artificial neural networks. Industrial Crops and Products, 146, Article 112149. https://doi.org/10.1016/j.indcrop.2020.112149

  • García, O. (1988). Growth modelling - A review. New Zealand Forestry, 33(3), 14-17.

  • Gompertz, B. (1825). XXIV. On the nature of the function expressive of the law of human mortality, and on a new mode of determining the value of life contingencies. In a letter to Francis Baily, Esq. FRS &c. Philosophical Transactions of the Royal Society of London, 115, 513-583. https://doi.org/10.1098/rspl.1815.0271

  • Gujarati, D. N., & Porter, D. C. (2011). Basic econometrics (5th Ed). AMGH Editora.

  • IBGE. (2018). Macrocaracterização - Tipos de Solos [Macrocharacterization - Soil types]. Instituto Brasileiro de Geografia e Estatística. https://portaldemapas.ibge.gov.br/portal.php#homepage

  • IBGE. (2021). Produção da extração vegetal e da silvicultura – PEVS, 2020 [Vegetal extraction and forestry production - PEVS, 2020]. Instituto Brasileiro de Geografia e Estatística. https://www.ibge.gov.br/estatisticas/economicas/agricultura-e-pecuaria/9105-producao-da-extracao-vegetal-e-da-silvicultura.html?edicao=29153&t=resultados

  • Liu, L., Lim, S., Shen, X., & Yebra, M. (2020). Assessment of generalized allometric models for aboveground biomass estimation: A case study in Australia. Computers and Electronics in Agriculture, 175, Article 105610. https://doi.org/10.1016/j.compag.2020.105610

  • Martins, E. R., Binoti, M. L. M. S., Leite, H. G., Binoti, D. H. B., & Dutra, G. C. (2015). Configuração de redes neurais artificiais para prognose da produção de povoamentos clonais de eucalipto [Configuration of artificial neural network for prognosis the production of eucalyptus clonal stands]. Revista Brasileira de Ciências Agrárias (Agrária), 10(4), 532-537. https://doi.org/10.5039/agraria.v10i4a5350

  • Mongus, D., Vilhar, U., Skudnik, M., Žalik, B., & Jesenko, D. (2018). Predictive analytics of tree growth based on complex networks of tree competition. Forest Ecology and Management, 425, 164-176. https://doi.org/10.1016/j.foreco.2018.05.039

  • Nieto, P. J. G., Torres, J. M., Fernández, M. A., & Galán, O. C. (2012). Support vector machines and neural networks used to evaluate paper manufactured using Eucalyptus globulus. Applied Mathematical Modelling, 36(12), 6137-6145. https://doi.org/10.1016/j.apm.2012.02.016

  • Penido, T. M. A., Lafetá, B. O., Nogueira, G. S., Alves, P. H., Gorgens, E. B., & Oliveira, M. L. R. (2020). Modelos de crescimento e produção para a estimativa volumétrica em povoamentos comerciais de eucalipto [Growth and production models for volumetric estimates in commercial eucalypt stands]. Scientia Forestalis, 48(128), Article e3340. https://doi.org/10.18671/scifor.v48n128.06

  • Pereira, K. D., Carneiro, A. P. S., Santos, G. R., Carneiro, A. C. O., Leite, H. G., & Borges, F. P. (2021). Study of the influence of wood properties on the charcoal production: Applying the random forest algorithm. Revista Árvore 45, Article e4502. http://dx.doi.org/10.1590/1806-908820210000002

  • Reis, L. P., de Souza, A. L., dos Reis, P. C. M., Mazzei, L., Soares, C. P. B., Torres, C. M. M. E., da Silva, L. F., Ruschel, A. R., Rêgo, L. J. S., & Leite, H. G. (2018). Estimation of mortality and survival of individual trees after harvesting wood using artificial neural networks in the amazon rain forest. Ecological Engineering, 112, 140-147. https://doi.org/10.1016/j.ecoleng.2017.12.014

  • Rodriguez, L. C. E., Bueno, A. R. S., & Rodrigues, F. (1997). Rotações de eucaliptos mais longas: análise volumétrica e econômica [Longer eucalypt rotations: volumetric and economic analysis]. Scientia Forestalis, 51(1), 15-28.

  • Salles, T. T., Leite, H. G., de Oliveira Neto, S. N., Soares, C. P. B., de Paiva, H. N., & dos Santos, F. L. (2012). Modelo de Clutter na modelagem de crescimento e produção de eucalipto em sistemas de integração lavoura-pecuária-floresta [Clutter model in modeling growth and yield of eucalyptus in crop livestock forest integration systems]. Pesquisa Agropecuária Brasileira, 47(2), 253-260. https://doi.org/10.1590/S0100-204X2012000200014

  • Scolforo, H. F., McTague, J. P., Burkhart, H., Roise, J., Campoe, O., & Stape, J. L. (2019a). Eucalyptus growth and yield system: Linking individual-tree and stand-level growth models in clonal Eucalypt plantations in Brazil. Forest Ecology and Management, 432, 1-16. https://doi.org/10.1016/j.foreco.2018.08.045

  • Scolforo, H. F., McTague, J. P., Burkhart, H., Roise, J., McCarter, J., Alvares, C. A., & Stape, J. L. (2019b). Stand-level growth and yield model system for clonal eucalypt plantations in Brazil that accounts for water availability. Forest Ecology and Management, 448, 22-33. https://doi.org/10.1016/j.foreco.2019.06.006

  • Sharma, R. P., Vacek, Z., Vacek, S., & Kučera, M. (2019). Modelling individual tree height–diameter relationships for multi-layered and multi-species forests in central Europe. Trees, 33(1), 103-119. https://doi.org/10.1007/s00468-018-1762-4

  • Silva, I. N., Spatti, D. H., & Flauzino, R. A. (2016). Redes Neurais Artificiais para engenharia e ciências aplicadas curso prático. (2a edição revisada e ampliada) [Artificial Neural Networks for engineering and applied sciences: practical course. (2nd edition revised and expanded)]. Artliber.

  • Silva, J. P. M., da Silva, M. L. M., de Mendonça, A. R., da Silva, G. F., de Barros Junior, A. A., da Silva, E. F., Aguiar, M. O., Santos, J. S., & Rodrigues, N. M. M. (2021). Prognosis of forest production using machine learning techniques. Information Processing in Agriculture, 1-14. https://doi.org/10.1016/j.inpa.2021.09.004

  • Stankova, T. V. (2016). A dynamic whole-stand growth model, derived from allometric relationships. Silva Fennica, 50(1), 1406. http://dx.doi.org/10.14214/sf.1406

  • Vescovi, L. B., Leite, H. G., Soares, C. P. B., de Oliveira, M. L. R., Binoti, D. H. B., Fardin, L. P., Silva, G. C. C., de Sousa Lopes, L. S., Leite, R. V., de Oliveira Neto, R. R., Silva, S. (2020). Effect of growth and yield modelling on forest regulation and earnings. African Journal of Agricultural Research, 16(7), 1050-1060. https://doi.org/10.5897/AJAR2020.14755

  • Vieira, G. C., de Mendonça, A. R., da Silva, G. F., Zanetti, S. S., da Silva, M. M., & dos Santos, A. R. (2018). Prognoses of diameter and height of trees of eucalyptus using artificial intelligence. Science of The Total Environment, 619, 1473-1481. https://doi.org/10.1016/j.scitotenv.2017.11.138

ISSN 0128-7702

e-ISSN 2231-8534

Article ID

JST-3072-2021

Download Full Article PDF

Share this article

Recent Articles