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Registro Completo |
Biblioteca(s): |
Embrapa Pesca e Aquicultura. |
Data corrente: |
13/12/2016 |
Data da última atualização: |
09/02/2017 |
Tipo da produção científica: |
Resumo em Anais de Congresso |
Autoria: |
FERREIRA JUNIOR, O. J.; BORTOLON, L.; BORGHI, E.; BORTOLON, E. S. O.; CAMARGO, F. P. de; SILVA, R. R. da; LIMA, A. de O.; SOUSA, J. P. de; PADUA, R. P. de; ANDRADE, C. A. O. de. |
Afiliação: |
OSVALDO JOSE FERREIRA JUNIOR, UFT, Gurupi, TO.; LEANDRO BORTOLON, CNPASA; EMERSON BORGHI, CNPMS; ELISANDRA SOLANGE OLIVEIRA BORTOLON, CNPASA; FRANCELINO PETENO DE CAMARGO, CNPASA; RUBENS RIBEIRO DA SILVA, UFT, Gurupi, TO.; ALAN DE ORNELAS LIMA, Católica do Tocantins, Palmas, TO.; JESSICA PEREIRA DE SOUSA, Católica do Tocantins, Palmas, TO.; ROSE PAMELLA DE PADUA, Católica do Tocantins, Palmas, TO.; CARLOS AUGUSTO OLIVEIRA DE ANDRADE, UFT, Gurupi,TO. |
Título: |
Altura de plantas de soja em função da correção do solo e sobressemeadura de forrageiras na soja. |
Ano de publicação: |
2016 |
Fonte/Imprenta: |
In: REUNIÃO BRASILEIRA DE FERTILIDADE DO SOLO E NUTRIÇÃO DE PLANTAS, 32.; REUNIÃO BRASILEIRA SOBRE MICORRIZAS, 16.; SIMPÓSIO BRASILEIRO DE MICROBIOLOGIA DO SOLO, 14.; REUNIÃO BRASILEIRA DE BIOLOGIA DO SOLO, 11., 2016, Goiânia. Rumo aos novos desafios: [anais]. Viçosa, MG: Sociedade Brasileira de Ciência do Solo, 2016. |
Páginas: |
p. 736. |
Idioma: |
Português |
Notas: |
FertBio 2016. |
Conteúdo: |
A integração lavoura-pecuária (ILP) diversifica a atividade agropecuária na propriedade rural, constituindo sistema de tal maneira que todas as culturas se beneficiem. Neste contexto, o objetivo desse estudo foi avaliar a influência da sobressemeadura de forrageiras na soja e a correção do solo na altura de planta (AP) da soja. |
Palavras-Chave: |
ILP. |
Thesagro: |
Cerrado; Glycine max; Soja; Solo. |
Categoria do assunto: |
F Plantas e Produtos de Origem Vegetal |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/149912/1/Altura-plantas.pdf
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Marc: |
LEADER 01478naa a2200313 a 4500 001 2058489 005 2017-02-09 008 2016 bl uuuu u00u1 u #d 100 1 $aFERREIRA JUNIOR, O. J. 245 $aAltura de plantas de soja em função da correção do solo e sobressemeadura de forrageiras na soja.$h[electronic resource] 260 $c2016 300 $ap. 736. 500 $aFertBio 2016. 520 $aA integração lavoura-pecuária (ILP) diversifica a atividade agropecuária na propriedade rural, constituindo sistema de tal maneira que todas as culturas se beneficiem. Neste contexto, o objetivo desse estudo foi avaliar a influência da sobressemeadura de forrageiras na soja e a correção do solo na altura de planta (AP) da soja. 650 $aCerrado 650 $aGlycine max 650 $aSoja 650 $aSolo 653 $aILP 700 1 $aBORTOLON, L. 700 1 $aBORGHI, E. 700 1 $aBORTOLON, E. S. O. 700 1 $aCAMARGO, F. P. de 700 1 $aSILVA, R. R. da 700 1 $aLIMA, A. de O. 700 1 $aSOUSA, J. P. de 700 1 $aPADUA, R. P. de 700 1 $aANDRADE, C. A. O. de 773 $tIn: REUNIÃO BRASILEIRA DE FERTILIDADE DO SOLO E NUTRIÇÃO DE PLANTAS, 32.; REUNIÃO BRASILEIRA SOBRE MICORRIZAS, 16.; SIMPÓSIO BRASILEIRO DE MICROBIOLOGIA DO SOLO, 14.; REUNIÃO BRASILEIRA DE BIOLOGIA DO SOLO, 11., 2016, Goiânia. Rumo aos novos desafios: [anais]. Viçosa, MG: Sociedade Brasileira de Ciência do Solo, 2016.
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Embrapa Pesca e Aquicultura (CNPASA) |
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Registro Completo
Biblioteca(s): |
Embrapa Arroz e Feijão; Embrapa Pantanal. |
Data corrente: |
28/06/2023 |
Data da última atualização: |
04/09/2023 |
Tipo da produção científica: |
Artigo em Anais de Congresso |
Autoria: |
SANTOS, B. Z.; SORIANO, B. M. A.; NARCISO, M. G.; SILVA, D. F.; CERRI, R. |
Afiliação: |
BRUNA ZAMITH SANTOS, UNIVERSIDADE FEDERAL DE SÃO CARLOS; BALBINA MARIA ARAUJO SORIANO, CPAP; MARCELO GONCALVES NARCISO, CNPAF; DIEGO FURTADO SILVA, USP; RICARDO CERRI, UNIVERSIDADE FEDERAL DE SÃO CARLOS. |
Título: |
A new time series framework for forest fire risk forecasting and classification. |
Ano de publicação: |
2023 |
Fonte/Imprenta: |
In: INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2023, Queensland, Australia. Proceedings. Illinois: International Neural Network Society, 2023. |
Idioma: |
Inglês |
Conteúdo: |
There's an increasing concern about the occurrence and spread of forest fires across the globe, as they contribute to greenhouse gas emissions and play a major influential role in economics and public health. Thus, there's a need for accurate methods to predict and classify forest fire risk. The main known forest fire risk indexes have limitations, such as not taking into account the unique characteristics of the biome in study, and not being able to predict forest fire risk for a given number of days in the future. This last aspect, in particular, is of utmost relevance. Addressing it allows for coordinated planning and action by proper authorities with adequate anticipation. Aiming to solve this problem, we present a new framework that applies Machine Learning methods for: (1) climatic variables forecasting; and (2) forest fire risk classification. For the first objective, different time series forecasting algorithms were tested. The forecasted variables are then used as input for the second objective, for which different classification algorithms were also tested. We evaluated our proposal using Brazilian Pantanal regional biome data from 1999 to 2019, where climatic variables were collected from ground meteorological stations, and fire occurrences (hotspots) were obtained from satellite images. The experiments considered 4 climatic variables and 5 forest fire risk classes. The results were evaluated based on the average correlation between (i) the prediction of forest fire risk classes and (ii) the observation of hotspots. Our proposal proved to be better or competitive with the main forest fire risk indexes, with the advantage of predicting fire risk for a given number of days in the future. MenosThere's an increasing concern about the occurrence and spread of forest fires across the globe, as they contribute to greenhouse gas emissions and play a major influential role in economics and public health. Thus, there's a need for accurate methods to predict and classify forest fire risk. The main known forest fire risk indexes have limitations, such as not taking into account the unique characteristics of the biome in study, and not being able to predict forest fire risk for a given number of days in the future. This last aspect, in particular, is of utmost relevance. Addressing it allows for coordinated planning and action by proper authorities with adequate anticipation. Aiming to solve this problem, we present a new framework that applies Machine Learning methods for: (1) climatic variables forecasting; and (2) forest fire risk classification. For the first objective, different time series forecasting algorithms were tested. The forecasted variables are then used as input for the second objective, for which different classification algorithms were also tested. We evaluated our proposal using Brazilian Pantanal regional biome data from 1999 to 2019, where climatic variables were collected from ground meteorological stations, and fire occurrences (hotspots) were obtained from satellite images. The experiments considered 4 climatic variables and 5 forest fire risk classes. The results were evaluated based on the average correlation between (i) the prediction of forest fi... Mostrar Tudo |
Thesagro: |
Análise de Risco; Fogo; Incêndio Florestal; Previsão do Tempo. |
Thesaurus NAL: |
Classification; Forest fires; Weather forecasting. |
Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
Marc: |
LEADER 02530nam a2200241 a 4500 001 2154743 005 2023-09-04 008 2023 bl uuuu u00u1 u #d 100 1 $aSANTOS, B. Z. 245 $aA new time series framework for forest fire risk forecasting and classification.$h[electronic resource] 260 $aIn: INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2023, Queensland, Australia. Proceedings. Illinois: International Neural Network Society$c2023 520 $aThere's an increasing concern about the occurrence and spread of forest fires across the globe, as they contribute to greenhouse gas emissions and play a major influential role in economics and public health. Thus, there's a need for accurate methods to predict and classify forest fire risk. The main known forest fire risk indexes have limitations, such as not taking into account the unique characteristics of the biome in study, and not being able to predict forest fire risk for a given number of days in the future. This last aspect, in particular, is of utmost relevance. Addressing it allows for coordinated planning and action by proper authorities with adequate anticipation. Aiming to solve this problem, we present a new framework that applies Machine Learning methods for: (1) climatic variables forecasting; and (2) forest fire risk classification. For the first objective, different time series forecasting algorithms were tested. The forecasted variables are then used as input for the second objective, for which different classification algorithms were also tested. We evaluated our proposal using Brazilian Pantanal regional biome data from 1999 to 2019, where climatic variables were collected from ground meteorological stations, and fire occurrences (hotspots) were obtained from satellite images. The experiments considered 4 climatic variables and 5 forest fire risk classes. The results were evaluated based on the average correlation between (i) the prediction of forest fire risk classes and (ii) the observation of hotspots. Our proposal proved to be better or competitive with the main forest fire risk indexes, with the advantage of predicting fire risk for a given number of days in the future. 650 $aClassification 650 $aForest fires 650 $aWeather forecasting 650 $aAnálise de Risco 650 $aFogo 650 $aIncêndio Florestal 650 $aPrevisão do Tempo 700 1 $aSORIANO, B. M. A. 700 1 $aNARCISO, M. G. 700 1 $aSILVA, D. F. 700 1 $aCERRI, R.
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