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

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Abstract

This study aimed to model the height of trees and volumetric production of eucalypts trees on the agrosilvopastoral systems (AGP) of Zona da Mata Mineira region, Brazil, using artificial neural network (ANN) and regression models to determine the best alternative. The data was obtained from five systems with different spatial arrangements (8 × 3 m, 10 × 3 m, 11 × 3 m, 12 × 3 m, 12 × 2 m, and 12 × 4 m), ages (5.5, 6.5 and 8 years) and genotypes, of which 122 sample trees were scaled. Hypsometric and volumetric models were adjusted considering no stratification or stratification by the AGP, spatial arrangement, and genotype. A multilayer perceptron ANN was trained using resilient propagation and skip layer training algorithms. The stratification variables used in the regression were used in the ANN as categorical variables. To estimate height of trees were used as continuous variables: diameter at breast height (dbh), dominant height (Dh), and age. To estimate volume were used as continuous variables: dbh, total height, and age. The AGPs’s mean annual increment at 5.5, 6.5 and 8 years of age ranged from 21 to 62 m3 ha−1 year−1. ANN was proven to be an efficient methodology for hypsometric and volumetric estimates of eucalypt in AGP in the study region.

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Acknowledgements

We acknowledge Conselho Nacional de Desenvolvimento Cientifico e Tecnológico (CNPq) for funding the research. To Agronomist Rogério Jacinto Gomes, to Professor Lino Roberto Ferreira, coordinators of the Forest Integration Zone-Farming-Livestock-Forest Circuit (Partnership EMATER-MG/Universidade Federal De Viçosa). To farmers for the availability of field demonstration units for data collection. To Amana Obolari, Bruno Schettini, Ricardo Pena, Maria Tereza Nunes, and Cristina Nolasco for their assistance in data collection. We would like to thank Editage (www.editage.com) for English language editing.

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  1. Department of Forest Engineering, Universidade Federal de Viçosa, Av Ph Rolfs s/n, Viçosa, Minas Gerais, 36570-000, Brazil

    Simone Silva, Sílvio Nolasco de Oliveira Neto, Helio Garcia Leite & Ricardo Rodrigues de Oliveira Neto

  2. Celulose Nipo-Brasileira S/A, Br 381 Road, s/n km 172, Belo Oriente, Minas Gerais, 35196-000, Brazil

    Aline Edwiges Mazon de Alcântara

  3. Universidade Estadual do Mato Grosso do Sul, Graziela Maciel Barroso Road Km 12, Aquidauana, Mato Grosso do Sul, 79200-000, Brazil

    Guilherme Silvério Aquino de Souza

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Conceptualization, S.S., S. N. O. N. and H. G. L.; Formal analysis, S.S., R. R. O. N., G. S. A. S., S. N. O. N. and H. G. L.; Methodology, S. S.;S.N.O. N. and H. G. L; Writing-original draft S. S.; Writing-review and editing, S. S., S. N. O. N., R. R. O.N., A. E. M. A., H. G. L.; G. S. A. S.

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Silva, S., de Oliveira Neto, S.N., Leite, H.G. et al. Productivity estimate using regression and artificial neural networks in small familiar areas with agrosilvopastoral systems. Agroforest Syst 94, 2081–2097 (2020). https://doi.org/10.1007/s10457-020-00526-1

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