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Registro Completo |
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Biblioteca(s): |
Embrapa Agrossilvipastoril. |
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Data corrente: |
06/03/2017 |
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Data da última atualização: |
04/10/2020 |
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Tipo da produção científica: |
Resumo em Anais de Congresso |
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Autoria: |
CRESTANI, S.; GEREMIA, E. V.; CABALLERO, J. D.; MONTEIRO, R. A. C.; MONTAGNER, D. B.; SILVA, S. C. da. |
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Afiliação: |
STEBEN CRESTANI, USP-ESALQ; ELIANA VERA GEREMIA, USP-ESALQ; JORGE DANIEL CABALLERO, USP-ESALQ; ROBERTA APARECIDA C MONTEIRO, CPAMT; DENISE BAPTAGLIN MONTAGNER, CNPGC; SILA CARNEIRO DA SILVA, USP-ESALQ. |
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Título: |
Effects of the deferment period on chemical composition of Piatã grass in silvipastoral systems. |
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Ano de publicação: |
2016 |
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Fonte/Imprenta: |
In: REUNIÃO ANUAL DA SOCIEDADE BRASILEIRA DE ZOOTECNIA, 53., 2016, Gramado. Produção animal para as novas gerações: anais. Gramado: SBZ, 2016. Não paginado. |
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Idioma: |
Inglês |
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Conteúdo: |
Deferred pasture can be harvest by grazing or mechanically. The mechanized harvest increases the efficiency of fodder utilization. However, these technique increases the portion of stem and dead material of the feed relative to the total forage grazed, witch compromises the quality of the forage. The objective of this experiment was to evaluate the ability of shaded pastures of Piatã brachiaria grass (Brachiaria brizantha cv. Piatã) to maintain chemical composition of the whole plant and determine the potential benefits in relation to full light condition. The experiment was performed at Embrapa Agrossilvipastoril, Sinop, MT, Brazil (11º51'43'' S, 55º35'27'' W, 384 m) from February to June 2014. A crop-livestock-forest integration area was divided into two light regimes: Piatã cultivated in the absence of trees (full natural light; FL); and Piatã cultivated under shade produced by four triple rows of trees (Eucaliptus H13 clone, 650 trees ha-1) with pasture between the rows (S2). Treatments correspond to combinations between: light regimes (FL and S2), two beginnings of rest period (R1 ? February 26, R2 ? March 28) and two grazing dates (G1 ? May 16, G2 ? June 16). These were applied to experimental units (15 x 4 m plots) in a 2x2x2 factorial arrangement, with four replications. Total rainfall and duration of deferment corresponded to 223 mm and 79 days (R1G1), 257 mm and 110 days (R1G2), 126 mm and 49 days (R2G1) and 150 mm and 80 days (R2G2), respectively. At the beginning of rest period (R1 and R2), plots were mowed at 10 cm and at the end of the deferment period (G1 and G2) four samples harvested (0.25 m2). These samples were mixed and a whole plant portion was separated and analyzed by Near Infrared Reflectance Spectroscopy (NIRS). Statistical analysis was performed using PROC Mixed of SAS at a 5% probability level. Light regime S2 showed 40% of the light transmittance of FL (100%). Crude protein was higher for S2 than FL (97.4 and 71.7 g DM kg-1) and for G1 than G2 (95.5 and 73.6 g DM kg-1). Neutral detergent fiber was higher for FL, R1 and G2 (714, 714 and 716 g DM kg-1) relative to S2, R2 and G1 (705, 704 and 702 g DM kg-1, respectively). Acid detergent fiber was higher for R1 than R2 (395 and 384 g DM kg-1) and for G2 than G1 (399 and 380 g DM kg-1). The organic matter digestibility was higher for G1 than G2 (579 and 538 g DM kg-1). Dead material was 12% higher for G2 than G1 and it was the main reason that G2 presented the worst values of the chemical analysis. In conclusion, although the results were interfered by light regimes and rest dates, the main effects on whole plant chemical composition was caused by prolonging of the deferment period into unfavorable season for the development of the plant. MenosDeferred pasture can be harvest by grazing or mechanically. The mechanized harvest increases the efficiency of fodder utilization. However, these technique increases the portion of stem and dead material of the feed relative to the total forage grazed, witch compromises the quality of the forage. The objective of this experiment was to evaluate the ability of shaded pastures of Piatã brachiaria grass (Brachiaria brizantha cv. Piatã) to maintain chemical composition of the whole plant and determine the potential benefits in relation to full light condition. The experiment was performed at Embrapa Agrossilvipastoril, Sinop, MT, Brazil (11º51'43'' S, 55º35'27'' W, 384 m) from February to June 2014. A crop-livestock-forest integration area was divided into two light regimes: Piatã cultivated in the absence of trees (full natural light; FL); and Piatã cultivated under shade produced by four triple rows of trees (Eucaliptus H13 clone, 650 trees ha-1) with pasture between the rows (S2). Treatments correspond to combinations between: light regimes (FL and S2), two beginnings of rest period (R1 ? February 26, R2 ? March 28) and two grazing dates (G1 ? May 16, G2 ? June 16). These were applied to experimental units (15 x 4 m plots) in a 2x2x2 factorial arrangement, with four replications. Total rainfall and duration of deferment corresponded to 223 mm and 79 days (R1G1), 257 mm and 110 days (R1G2), 126 mm and 49 days (R2G1) and 150 mm and 80 days (R2G2), respectively. At the beginning... Mostrar Tudo |
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Thesaurus Nal: |
Chemical composition. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 03472naa a2200193 a 4500
001 2066275
005 2020-10-04
008 2016 bl uuuu u00u1 u #d
100 1 $aCRESTANI, S.
245 $aEffects of the deferment period on chemical composition of Piatã grass in silvipastoral systems.$h[electronic resource]
260 $c2016
520 $aDeferred pasture can be harvest by grazing or mechanically. The mechanized harvest increases the efficiency of fodder utilization. However, these technique increases the portion of stem and dead material of the feed relative to the total forage grazed, witch compromises the quality of the forage. The objective of this experiment was to evaluate the ability of shaded pastures of Piatã brachiaria grass (Brachiaria brizantha cv. Piatã) to maintain chemical composition of the whole plant and determine the potential benefits in relation to full light condition. The experiment was performed at Embrapa Agrossilvipastoril, Sinop, MT, Brazil (11º51'43'' S, 55º35'27'' W, 384 m) from February to June 2014. A crop-livestock-forest integration area was divided into two light regimes: Piatã cultivated in the absence of trees (full natural light; FL); and Piatã cultivated under shade produced by four triple rows of trees (Eucaliptus H13 clone, 650 trees ha-1) with pasture between the rows (S2). Treatments correspond to combinations between: light regimes (FL and S2), two beginnings of rest period (R1 ? February 26, R2 ? March 28) and two grazing dates (G1 ? May 16, G2 ? June 16). These were applied to experimental units (15 x 4 m plots) in a 2x2x2 factorial arrangement, with four replications. Total rainfall and duration of deferment corresponded to 223 mm and 79 days (R1G1), 257 mm and 110 days (R1G2), 126 mm and 49 days (R2G1) and 150 mm and 80 days (R2G2), respectively. At the beginning of rest period (R1 and R2), plots were mowed at 10 cm and at the end of the deferment period (G1 and G2) four samples harvested (0.25 m2). These samples were mixed and a whole plant portion was separated and analyzed by Near Infrared Reflectance Spectroscopy (NIRS). Statistical analysis was performed using PROC Mixed of SAS at a 5% probability level. Light regime S2 showed 40% of the light transmittance of FL (100%). Crude protein was higher for S2 than FL (97.4 and 71.7 g DM kg-1) and for G1 than G2 (95.5 and 73.6 g DM kg-1). Neutral detergent fiber was higher for FL, R1 and G2 (714, 714 and 716 g DM kg-1) relative to S2, R2 and G1 (705, 704 and 702 g DM kg-1, respectively). Acid detergent fiber was higher for R1 than R2 (395 and 384 g DM kg-1) and for G2 than G1 (399 and 380 g DM kg-1). The organic matter digestibility was higher for G1 than G2 (579 and 538 g DM kg-1). Dead material was 12% higher for G2 than G1 and it was the main reason that G2 presented the worst values of the chemical analysis. In conclusion, although the results were interfered by light regimes and rest dates, the main effects on whole plant chemical composition was caused by prolonging of the deferment period into unfavorable season for the development of the plant.
650 $aChemical composition
700 1 $aGEREMIA, E. V.
700 1 $aCABALLERO, J. D.
700 1 $aMONTEIRO, R. A. C.
700 1 $aMONTAGNER, D. B.
700 1 $aSILVA, S. C. da
773 $tIn: REUNIÃO ANUAL DA SOCIEDADE BRASILEIRA DE ZOOTECNIA, 53., 2016, Gramado. Produção animal para as novas gerações: anais. Gramado: SBZ, 2016. Não paginado.
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Embrapa Agrossilvipastoril (CPAMT) |
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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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Circulação/Nível: |
A - 1 |
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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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