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Registro Completo |
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Biblioteca(s): |
Embrapa Amazônia Oriental. |
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Data corrente: |
31/01/2018 |
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Data da última atualização: |
02/05/2018 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
REIS, L. P.; SOUZA, A. L. de; REIS, P. C. M. dos; FREITAS, L. J. M. de; SOARES, C. P. B.; TORRES, C. M. M. E.; SILVA, L. F. da; RUSCHEL, A. R.; RÊGO, L. J. S.; LEITE, H. G. |
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Afiliação: |
Leonardo Pequeno Reis, Instituto de Desenvolvimento Sustentável Mamirauá; Agostinho Lopes de Souza, UFV; Pamella Carolline Marques dos Reis, UFV; LUCAS JOSE MAZZEI DE FREITAS, CPATU; Carlos Pedro Boechat Soares, UFV; Carlos Moreira Miquelino Eleto Torres, UFV; Liniker Fernandes da Silva, Universidade Federal do Recôncavo da Bahia; ADEMIR ROBERTO RUSCHEL, CPATU; Lyvia Julienne Sousa Rêgo, UFV; Helio Garcia Leite, UFV. |
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Título: |
Estimation of mortality and survival of individual trees after harvesting wood using artificial neural networks in the amazon rain forest. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
Ecological Engineering, v. 112, p. 140-147, Mar. 2018. |
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DOI: |
https://doi.org/10.1016/j.ecoleng.2017.12.014 |
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Idioma: |
Inglês |
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Conteúdo: |
Modeling individual trees in tropical rain forests in the Amazon allows for the safe use of scarce resources in a sustainable way. Unfortunately, in the Brazilian Amazon, rain forest growth and production models are not yet used to estimate future forest stock. Thus, forest management plans do not present technical-scientific support that guarantees sustainable production of wood throughout the cutting cycle. Therefore, this work aims to estimate the survival and mortality of individual trees in a selectively harvested forest using Artificial Neural Networks (ANN) to support silvicultural decisions in forest management in the Amazon rain forest. In 1979, a selective harvest was carried out, with 72.5 m3 ha-1 in an area of 64 ha in Floresta Nacional do Tapajós, in the state of Pará, Brazil. In 1981, 36 permanent plots were installed at random and inventoried. Nine successive measurements were carried from 1982 to 2012. In the modeling, classification, survival, and mortality, training and ANN testing were performed, using input variables such as: different semi-distance-independent competition indices (DSICI), diameter measured (dbh), forest class (FC), trunk identification class (TIC), competition index (CI), growth groups (GG), liana infestation intensity (liana); and crown lighting (CL); Damage to tree (D) and tree rotting (R). The categorical output variables (Classification) were Dead or Surviving tree. Overall efficiency of the classification was above 89% in training and above 90% in the test for all ANNs. Survival classification hit rate was above 99% in the test and training for all ANNs but the mortality score was low, with hit rates below 6%. The overall Kappa coefficient was below 8% for all ANNs (ranked ?poor?) but all ANNs were above 55% in the survival classification (ranked ?good?). ANN estimates the individual survival of trees more accurately but this does not occur with mortality, which is a rarer event than survival. MenosModeling individual trees in tropical rain forests in the Amazon allows for the safe use of scarce resources in a sustainable way. Unfortunately, in the Brazilian Amazon, rain forest growth and production models are not yet used to estimate future forest stock. Thus, forest management plans do not present technical-scientific support that guarantees sustainable production of wood throughout the cutting cycle. Therefore, this work aims to estimate the survival and mortality of individual trees in a selectively harvested forest using Artificial Neural Networks (ANN) to support silvicultural decisions in forest management in the Amazon rain forest. In 1979, a selective harvest was carried out, with 72.5 m3 ha-1 in an area of 64 ha in Floresta Nacional do Tapajós, in the state of Pará, Brazil. In 1981, 36 permanent plots were installed at random and inventoried. Nine successive measurements were carried from 1982 to 2012. In the modeling, classification, survival, and mortality, training and ANN testing were performed, using input variables such as: different semi-distance-independent competition indices (DSICI), diameter measured (dbh), forest class (FC), trunk identification class (TIC), competition index (CI), growth groups (GG), liana infestation intensity (liana); and crown lighting (CL); Damage to tree (D) and tree rotting (R). The categorical output variables (Classification) were Dead or Surviving tree. Overall efficiency of the classification was above 89% i... Mostrar Tudo |
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Palavras-Chave: |
Gestão florestal; Inteligência artificial; Modelagem. |
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Thesagro: |
Floresta. |
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Categoria do assunto: |
K Ciência Florestal e Produtos de Origem Vegetal |
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Marc: |
LEADER 02910naa a2200289 a 4500
001 2086820
005 2018-05-02
008 2018 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1016/j.ecoleng.2017.12.014$2DOI
100 1 $aREIS, L. P.
245 $aEstimation of mortality and survival of individual trees after harvesting wood using artificial neural networks in the amazon rain forest.$h[electronic resource]
260 $c2018
520 $aModeling individual trees in tropical rain forests in the Amazon allows for the safe use of scarce resources in a sustainable way. Unfortunately, in the Brazilian Amazon, rain forest growth and production models are not yet used to estimate future forest stock. Thus, forest management plans do not present technical-scientific support that guarantees sustainable production of wood throughout the cutting cycle. Therefore, this work aims to estimate the survival and mortality of individual trees in a selectively harvested forest using Artificial Neural Networks (ANN) to support silvicultural decisions in forest management in the Amazon rain forest. In 1979, a selective harvest was carried out, with 72.5 m3 ha-1 in an area of 64 ha in Floresta Nacional do Tapajós, in the state of Pará, Brazil. In 1981, 36 permanent plots were installed at random and inventoried. Nine successive measurements were carried from 1982 to 2012. In the modeling, classification, survival, and mortality, training and ANN testing were performed, using input variables such as: different semi-distance-independent competition indices (DSICI), diameter measured (dbh), forest class (FC), trunk identification class (TIC), competition index (CI), growth groups (GG), liana infestation intensity (liana); and crown lighting (CL); Damage to tree (D) and tree rotting (R). The categorical output variables (Classification) were Dead or Surviving tree. Overall efficiency of the classification was above 89% in training and above 90% in the test for all ANNs. Survival classification hit rate was above 99% in the test and training for all ANNs but the mortality score was low, with hit rates below 6%. The overall Kappa coefficient was below 8% for all ANNs (ranked ?poor?) but all ANNs were above 55% in the survival classification (ranked ?good?). ANN estimates the individual survival of trees more accurately but this does not occur with mortality, which is a rarer event than survival.
650 $aFloresta
653 $aGestão florestal
653 $aInteligência artificial
653 $aModelagem
700 1 $aSOUZA, A. L. de
700 1 $aREIS, P. C. M. dos
700 1 $aFREITAS, L. J. M. de
700 1 $aSOARES, C. P. B.
700 1 $aTORRES, C. M. M. E.
700 1 $aSILVA, L. F. da
700 1 $aRUSCHEL, A. R.
700 1 $aRÊGO, L. J. S.
700 1 $aLEITE, H. G.
773 $tEcological Engineering$gv. 112, p. 140-147, Mar. 2018.
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Registro original: |
Embrapa Amazônia Oriental (CPATU) |
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Biblioteca |
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Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
URL |
Voltar
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Registro Completo
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Biblioteca(s): |
Embrapa Soja; Embrapa Trigo. |
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Data corrente: |
14/12/2022 |
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Data da última atualização: |
14/12/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
MILIOLI, A. S.; MEIRA, D.; PANHO, M. C.; MANDELLA, L. A.; WOYANN, L. G.; TODESCHINI, M. H.; ZDZIARSKI, A. D.; CAMPAGNOLLI, O. R.; MENEGAZZI, C. P.; COLONELLI, L. L.; FERNANDES, R. A. T.; MELO, C. L. P. de; OLIVEIRA, M. F. de; BERTAGNOLLI, P. F.; ARIAS, C. A. A.; GIASSON, N. F.; MATSUMOTO, M. N.; QUIROGA, M; SILVA, R. R.; BERTAN, I.; CAPELIN, M. A.; MATEI, G.; BENIN, G. |
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Afiliação: |
ANDERSON SIMIONATO MILIOLI, Universidade Tecnológica Federal do Paraná/Pato Branco; DANIELA MEIRA, Universidade Tecnológica Federal do Paraná/Pato Branco; MAIARA CECÍLIA PANHO, Universidade Tecnológica Federal do Paraná/Pato Branco; LAURA ALEXANDRA MADELLA, Universidade Tecnológica Federal do Paraná/Pato Branco; LEOMAR GUILHERME WOYANN, Universidade Tecnológica Federal do Paraná/Pato Branco; MATHEUS HENRIQUE TODESCHINI, TMG Tropical Melhoramento e Genética; ANDREI DANIEL ZDZIARSKI, GDM Seeds; OTÁVIO RAMOS CAMPAGNOLLI, Universidade Tecnológica Federal do Paraná/Pato Branco; CAROLINE PATRÍCIA MENEGAZZI, Universidade Tecnológica Federal do Paraná/Pato Branco; LUCAS LEITE COLONELLI, Universidade Tecnológica Federal do Paraná/Pato Branco; ROGÊ AFONSO TOLENTINO FERNANDES, Universidade Tecnológica Federal do Paraná/Pato Branco; CARLOS LASARO PEREIRA DE MELO, CNPSO; MARCELO FERNANDES DE OLIVEIRA, CNPSO; PAULO FERNANDO BERTAGNOLLI, CNPT; CARLOS ALBERTO ARRABAL ARIAS, CNPSO; NIZIO FERNANDO GIASSON, GDM Seeds; MARCOS NORIO MATSUMOTO, GDM Seeds; MARCOS QUIROGA, GDM Seeds; RAPHAEL ROSSI SILVA, TMG Tropical Melhoramento e Genética; IVANDRO BERTAN, Syngenta; MARCIO ANDREI CAPELIN, Syngenta; GILVANI MATEI, Syngenta; GIOVANI BENIN, Universidade Tecnológica Federal do Paraná/Pato Branco. |
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Título: |
Genetic improvement of soybeans in Brazil: South and Midwest regions |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
Crop Science, v. 62, n. 6, p. 2276-2293, 2022. |
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DOI: |
https://doi.org/10.1002/csc2.20820 |
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Idioma: |
Inglês |
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Conteúdo: |
Abstract: Soybean [Glycine max (L.) Merril] is one of the main crops produced worldwide, and on-farm yields have increased considerably in the last decades in Brazil. We evaluated the genetic gain for agronomic, phenological, and end-use quality traits in 29 cultivars in the South Region, and in 38 cultivars in the Midwest Region in Brazil, released from 1966 to 2011. Field trials were conducted in Macroregions 1, 2, and 4, in 2016?2017, 2017?2018, and 2018?2019 crop seasons. The best linear unbiased predictors (BLUP) of the cultivars were obtained for each trait using a linear model. The BLUPs were regressed with the year of release using linear and quadratic regression models. The rates of genetic gain for seed yield ranged from 11.98 to 15.31 kg ha?1 yr?1 (0.33 to 0.42% yr?1) in the South Region, and from 13.58 to 21.84 kg ha?1 yr?1 (0.47 to 0.77% yr?1) in the Midwest Region. New cultivars presented taller plants and more seed oil content, oil and protein yield, and lower seed weight, days to flowering, days to maturity, and seed protein content than old cultivars in the South Region, although with differences between the Macroregions. In the Midwest Region, new cultivars showed higher seed oil content, oil and protein yield, and lower bottom pod height and seed protein content than old cultivars. Our results showed that breeding programs have been efficient to improve soybean yield and other traits across the years, without yield plateaus in sight. |
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Thesagro: |
Melhoramento Genético Vegetal; Produtividade; Soja. |
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Thesaurus NAL: |
Plant genetics; Soybeans. |
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Categoria do assunto: |
--
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1149696/1/Genetic-Improvement-of-Soybeans-in-Brazil-Final-Published-Version.pdf
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Marc: |
LEADER 02735naa a2200457 a 4500
001 2149696
005 2022-12-14
008 2022 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1002/csc2.20820$2DOI
100 1 $aMILIOLI, A. S.
245 $aGenetic improvement of soybeans in Brazil$bSouth and Midwest regions$h[electronic resource]
260 $c2022
520 $aAbstract: Soybean [Glycine max (L.) Merril] is one of the main crops produced worldwide, and on-farm yields have increased considerably in the last decades in Brazil. We evaluated the genetic gain for agronomic, phenological, and end-use quality traits in 29 cultivars in the South Region, and in 38 cultivars in the Midwest Region in Brazil, released from 1966 to 2011. Field trials were conducted in Macroregions 1, 2, and 4, in 2016?2017, 2017?2018, and 2018?2019 crop seasons. The best linear unbiased predictors (BLUP) of the cultivars were obtained for each trait using a linear model. The BLUPs were regressed with the year of release using linear and quadratic regression models. The rates of genetic gain for seed yield ranged from 11.98 to 15.31 kg ha?1 yr?1 (0.33 to 0.42% yr?1) in the South Region, and from 13.58 to 21.84 kg ha?1 yr?1 (0.47 to 0.77% yr?1) in the Midwest Region. New cultivars presented taller plants and more seed oil content, oil and protein yield, and lower seed weight, days to flowering, days to maturity, and seed protein content than old cultivars in the South Region, although with differences between the Macroregions. In the Midwest Region, new cultivars showed higher seed oil content, oil and protein yield, and lower bottom pod height and seed protein content than old cultivars. Our results showed that breeding programs have been efficient to improve soybean yield and other traits across the years, without yield plateaus in sight.
650 $aPlant genetics
650 $aSoybeans
650 $aMelhoramento Genético Vegetal
650 $aProdutividade
650 $aSoja
700 1 $aMEIRA, D.
700 1 $aPANHO, M. C.
700 1 $aMANDELLA, L. A.
700 1 $aWOYANN, L. G.
700 1 $aTODESCHINI, M. H.
700 1 $aZDZIARSKI, A. D.
700 1 $aCAMPAGNOLLI, O. R.
700 1 $aMENEGAZZI, C. P.
700 1 $aCOLONELLI, L. L.
700 1 $aFERNANDES, R. A. T.
700 1 $aMELO, C. L. P. de
700 1 $aOLIVEIRA, M. F. de
700 1 $aBERTAGNOLLI, P. F.
700 1 $aARIAS, C. A. A.
700 1 $aGIASSON, N. F.
700 1 $aMATSUMOTO, M. N.
700 1 $aQUIROGA, M
700 1 $aSILVA, R. R.
700 1 $aBERTAN, I.
700 1 $aCAPELIN, M. A.
700 1 $aMATEI, G.
700 1 $aBENIN, G.
773 $tCrop Science$gv. 62, n. 6, p. 2276-2293, 2022.
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