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Registro Completo |
Biblioteca(s): |
Embrapa Agropecuária Oeste; Embrapa Clima Temperado; Embrapa Soja; Embrapa Trigo; Embrapa Unidades Centrais. |
Data corrente: |
27/11/1998 |
Data da última atualização: |
03/05/2018 |
Tipo da produção científica: |
Documentos |
Autoria: |
BACALTCHUK, B. |
Afiliação: |
CNPT. |
Título: |
Visão estratégica para a triticultura brasileira. |
Ano de publicação: |
1998 |
Fonte/Imprenta: |
Passo Fundo: EMBRAPA-CNPT, 1998. |
Páginas: |
22 p. |
Série: |
(EMBRAPA-CNPT. Documentos, 46). |
ISSN: |
0101-6644 |
Idioma: |
Português |
Conteúdo: |
Visao estrategica para a triticultura brasileira: Embrapa Trigo; A triticultura brasileira; Seguranca; Custo; Sustentabilidade; Desenvolvimento; Acoes politicas; Semente; Preco de comercializacao; Seguro; Credito; Futuro da triticultura. |
Palavras-Chave: |
Brasil; Rio Grande do Sul. |
Thesagro: |
Trigo. |
Thesaurus Nal: |
Brazil; wheat. |
Categoria do assunto: |
-- F Plantas e Produtos de Origem Vegetal |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/84097/1/CNPT-DOCUMENTOS-46-ESTRATEGIA-PARA-A-TRITICULTURA-BRASILEIRA-FL-13399.pdf
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Marc: |
LEADER 00743nam a2200205 a 4500 001 1850003 005 2018-05-03 008 1998 bl uuuu u0uu1 u #d 022 $a0101-6644 100 1 $aBACALTCHUK, B. 245 $aVisão estratégica para a triticultura brasileira. 260 $aPasso Fundo: EMBRAPA-CNPT$c1998 300 $a22 p. 490 $a(EMBRAPA-CNPT. Documentos, 46). 520 $aVisao estrategica para a triticultura brasileira: Embrapa Trigo; A triticultura brasileira; Seguranca; Custo; Sustentabilidade; Desenvolvimento; Acoes politicas; Semente; Preco de comercializacao; Seguro; Credito; Futuro da triticultura. 650 $aBrazil 650 $awheat 650 $aTrigo 653 $aBrasil 653 $aRio Grande do Sul
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Registro original: |
Embrapa Trigo (CNPT) |
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Registro Completo
Biblioteca(s): |
Embrapa Amapá; Embrapa Amazônia Oriental. |
Data corrente: |
18/11/2019 |
Data da última atualização: |
07/12/2023 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 2 |
Autoria: |
TOURNE, D. C. M.; BALLESTER, M. V. R.; JAMES, P. M. A.; MARTORANO, L. G.; GUEDES, M. C.; THOMAS, E. |
Afiliação: |
DAIANA C. M. TOURNE, USP; MARIA V. R. BALLESTER, USP; PATRICK M. A. JAMES, UNIVERSITY OF MONTRÉAL; LUCIETA GUERREIRO MARTORANO, CPATU; MARCELINO CARNEIRO GUEDES, CPAF-AP; EVERT THOMAS, BIOVERSITY INTERNATIONAL, REGIONAL OFFICE FOR THE AMERICAS. |
Título: |
Strategies to optimize modeling habitat suitability of Bertholletia excelsa in the Pan-Amazonia. |
Ano de publicação: |
2019 |
Fonte/Imprenta: |
Ecology and Evolution, v. 9, n. 22, p. 12357-12960, Nov. 2019. |
DOI: |
https://doi.org/10.1002/ece3.5726 |
Idioma: |
Inglês |
Conteúdo: |
Aim: Amazon-nut (Bertholletia excelsa) is a hyperdominant and protected tree species, playing a keystone role in nutrient cycling and ecosystem service provision in Amazonia. Our main goal was to develop a robust habitat suitability model of Amazon-nut and to identify the most important predictor variables to support conservation and tree planting decisions. Localization: Amazon region, South America. Methods: We collected 3,325 unique Amazon-nut records and assembled >100 spatial predictor variables organized across climatic, edaphic, and geophysical categories. We compared suitability models using variables (a) selected through statistical techniques; (b) recommended by experts; and (c) integrating both approaches (a and b). We applied different spatial filtering scenarios to reduce overfitting. We additionally fine-tuned MAXENT settings to our data. The best model was selected through quantitative and qualitative assessments. Results: Principal component analysis based on expert recommendations was the most appropriate method for predictor selection. Elevation, coarse soil fragments, clay, slope, and annual potential evapotranspiration were the most important predictors. Their relative contribution to the best model amounted to 75%. Filtering of the presences within a radius of 10 km displayed lowest overfitting, a satisfactory omission rate and the most symmetric distribution curve. Our findings suggest that under current environmental conditions, suitable habitat for Amazon-nut is found across 2.3 million km2, that is, 32% of the Amazon Biome. Main conclusion: The combination of statistical techniques with expert knowledge improved the quality of our suitability model. Topographic and soil variables were the most important predictors. The combination of predictor variable selection, fine-tuning of model parameters and spatial filtering was critical for the construction of a reliable habitat suitability model. MenosAim: Amazon-nut (Bertholletia excelsa) is a hyperdominant and protected tree species, playing a keystone role in nutrient cycling and ecosystem service provision in Amazonia. Our main goal was to develop a robust habitat suitability model of Amazon-nut and to identify the most important predictor variables to support conservation and tree planting decisions. Localization: Amazon region, South America. Methods: We collected 3,325 unique Amazon-nut records and assembled >100 spatial predictor variables organized across climatic, edaphic, and geophysical categories. We compared suitability models using variables (a) selected through statistical techniques; (b) recommended by experts; and (c) integrating both approaches (a and b). We applied different spatial filtering scenarios to reduce overfitting. We additionally fine-tuned MAXENT settings to our data. The best model was selected through quantitative and qualitative assessments. Results: Principal component analysis based on expert recommendations was the most appropriate method for predictor selection. Elevation, coarse soil fragments, clay, slope, and annual potential evapotranspiration were the most important predictors. Their relative contribution to the best model amounted to 75%. Filtering of the presences within a radius of 10 km displayed lowest overfitting, a satisfactory omission rate and the most symmetric distribution curve. Our findings suggest that under current environmental conditions, suitable habitat for Am... Mostrar Tudo |
Palavras-Chave: |
Análise de componentes principais; Avaliação de modelo; Conhecimento especializado; Entropia máxima; Expert knowledge; Filtragem espacial; Maximum entropy; Model evaluation; Modelo de distribuição de espécie; Protected Amazonian species; Spatial filtering; Species distribution model. |
Thesagro: |
Castanha. |
Thesaurus NAL: |
Principal component analysis. |
Categoria do assunto: |
K Ciência Florestal e Produtos de Origem Vegetal |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1159297/1/Strategies-to-optimize.pdf
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/204836/1/CPAF-AP-2019-Strategies-to-optimize-modeling-habitat.pdf
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Marc: |
LEADER 03112naa a2200361 a 4500 001 2159297 005 2023-12-07 008 2019 bl uuuu u00u1 u #d 024 7 $ahttps://doi.org/10.1002/ece3.5726$2DOI 100 1 $aTOURNE, D. C. M. 245 $aStrategies to optimize modeling habitat suitability of Bertholletia excelsa in the Pan-Amazonia.$h[electronic resource] 260 $c2019 520 $aAim: Amazon-nut (Bertholletia excelsa) is a hyperdominant and protected tree species, playing a keystone role in nutrient cycling and ecosystem service provision in Amazonia. Our main goal was to develop a robust habitat suitability model of Amazon-nut and to identify the most important predictor variables to support conservation and tree planting decisions. Localization: Amazon region, South America. Methods: We collected 3,325 unique Amazon-nut records and assembled >100 spatial predictor variables organized across climatic, edaphic, and geophysical categories. We compared suitability models using variables (a) selected through statistical techniques; (b) recommended by experts; and (c) integrating both approaches (a and b). We applied different spatial filtering scenarios to reduce overfitting. We additionally fine-tuned MAXENT settings to our data. The best model was selected through quantitative and qualitative assessments. Results: Principal component analysis based on expert recommendations was the most appropriate method for predictor selection. Elevation, coarse soil fragments, clay, slope, and annual potential evapotranspiration were the most important predictors. Their relative contribution to the best model amounted to 75%. Filtering of the presences within a radius of 10 km displayed lowest overfitting, a satisfactory omission rate and the most symmetric distribution curve. Our findings suggest that under current environmental conditions, suitable habitat for Amazon-nut is found across 2.3 million km2, that is, 32% of the Amazon Biome. Main conclusion: The combination of statistical techniques with expert knowledge improved the quality of our suitability model. Topographic and soil variables were the most important predictors. The combination of predictor variable selection, fine-tuning of model parameters and spatial filtering was critical for the construction of a reliable habitat suitability model. 650 $aPrincipal component analysis 650 $aCastanha 653 $aAnálise de componentes principais 653 $aAvaliação de modelo 653 $aConhecimento especializado 653 $aEntropia máxima 653 $aExpert knowledge 653 $aFiltragem espacial 653 $aMaximum entropy 653 $aModel evaluation 653 $aModelo de distribuição de espécie 653 $aProtected Amazonian species 653 $aSpatial filtering 653 $aSpecies distribution model 700 1 $aBALLESTER, M. V. R. 700 1 $aJAMES, P. M. A. 700 1 $aMARTORANO, L. G. 700 1 $aGUEDES, M. C. 700 1 $aTHOMAS, E. 773 $tEcology and Evolution$gv. 9, n. 22, p. 12357-12960, Nov. 2019.
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Embrapa Amazônia Oriental (CPATU) |
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