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 | Acesso ao texto completo restrito à biblioteca da Embrapa Agricultura Digital. Para informações adicionais entre em contato com cnptia.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
07/12/2018 |
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Data da última atualização: |
07/01/2020 |
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Tipo da produção científica: |
Resumo em Anais de Congresso |
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Autoria: |
NORONHA, R. L.; SOARES, M. D. R.; OLIVEIRA, I. N. de; FARHATE, C. V. V.; SOUZA, Z. M. de; OLIVEIRA, S. R. de M. |
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Afiliação: |
RENATO LÓPEZ NORONHA, Unicamp; MARCELO DAYRON RODRIGUES SOARES, Unicamp; INGRID NEHMI DE OLIVEIRA, Unicamp; CAMILA VIANA VIEIRA FARHATE, Unicamp; ZIGOMAR MENEZES DE SOUZA, Unicamp; STANLEY ROBSON DE MEDEIROS OLIVEIRA, CNPTIA. |
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Título: |
Soil carbon stock predictive models on archaeological black lands - natural and transformed. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
In: WORLD CONGRESS OF SOIL SCIENCE, 21., 2018, Rio de Janeiro. Soil science: beyond food and fuel: abstracts. Viçosa, MG: SBCS, 2018. |
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Páginas: |
Não paginado. |
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Idioma: |
Inglês |
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Notas: |
WCSS 2018. |
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Conteúdo: |
In the Amazon region, types of soil known as Archaeological Black Lands (ABL) present anthropic horizon A and are associated with prolonged human occupation by indigenous societies from the pre-Columbian period, where chemical and physical attributes have better quality than other types of soil in the Amazon, setting a large organic carbon reservoir. However, the conversion of these natural ecosystems into cultivated environments make emerge changes in soil carbon dynamics, often leading to a decline in soil organic carbon content. Therefore, our aim was to use data mining techniques to generate predictive models for the effect of soil use on carbon stock in natural and transformed areas of Archaeological Black Lands. We carried out our experiment in Manicoré and Apuí, Amazonas State, Brazil. After field data collection and laboratory analysis, we obtained a set of data consisting of 21 attributes, 20 predictive attributes consisting of 13 soil physical attributes, 6 soil chemical attributes, 1 soil use related attribute, and 1 response variable, referring to soil carbon stock (SCS), which is the classification target. Due to the large number of attributes, we performed a selection procedure to eliminate attributes of low correlation to the response variable. For data classification, we used the binary induction technique of the decision tree through software Weka 3.8. The results obtained showed that for the depth of 0.00-0.05 and 0.05-0.10 m, the best selected subset was determined using the Wrapper method for attribute selection. In the depth of 0.00-0.05 m, we generated a model of 79% accuracy containing only six rules, including, as the most important classification attribute was soil use. For the depth of 0.05-0.10 m, we generated an eight-rule decision tree of 74% accuracy including sand as the most important attribute. In this context, we highlight the Wrapper method efficiency to select subsets of predictive attributes, capable to generate more understandable decision trees, using a smaller number of attributes in the classification process, making it faster and with a lower computational cost. In addition, data mining techniques were efficient at providing predictive models capable to assist the decision-making process on possible management practices with the potential to conserve or increase soil carbon stock in archeological black lands. MenosIn the Amazon region, types of soil known as Archaeological Black Lands (ABL) present anthropic horizon A and are associated with prolonged human occupation by indigenous societies from the pre-Columbian period, where chemical and physical attributes have better quality than other types of soil in the Amazon, setting a large organic carbon reservoir. However, the conversion of these natural ecosystems into cultivated environments make emerge changes in soil carbon dynamics, often leading to a decline in soil organic carbon content. Therefore, our aim was to use data mining techniques to generate predictive models for the effect of soil use on carbon stock in natural and transformed areas of Archaeological Black Lands. We carried out our experiment in Manicoré and Apuí, Amazonas State, Brazil. After field data collection and laboratory analysis, we obtained a set of data consisting of 21 attributes, 20 predictive attributes consisting of 13 soil physical attributes, 6 soil chemical attributes, 1 soil use related attribute, and 1 response variable, referring to soil carbon stock (SCS), which is the classification target. Due to the large number of attributes, we performed a selection procedure to eliminate attributes of low correlation to the response variable. For data classification, we used the binary induction technique of the decision tree through software Weka 3.8. The results obtained showed that for the depth of 0.00-0.05 and 0.05-0.10 m, the best selected subset was d... Mostrar Tudo |
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Palavras-Chave: |
Anthropic soils; Árvore de decisão; Data mining techniques; Decision tree; Mineração de dados; Soil management system. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 03289nam a2200265 a 4500
001 2100986
005 2020-01-07
008 2018 bl uuuu u00u1 u #d
100 1 $aNORONHA, R. L.
245 $aSoil carbon stock predictive models on archaeological black lands - natural and transformed.$h[electronic resource]
260 $aIn: WORLD CONGRESS OF SOIL SCIENCE, 21., 2018, Rio de Janeiro. Soil science: beyond food and fuel: abstracts. Viçosa, MG: SBCS$c2018
300 $aNão paginado.
500 $aWCSS 2018.
520 $aIn the Amazon region, types of soil known as Archaeological Black Lands (ABL) present anthropic horizon A and are associated with prolonged human occupation by indigenous societies from the pre-Columbian period, where chemical and physical attributes have better quality than other types of soil in the Amazon, setting a large organic carbon reservoir. However, the conversion of these natural ecosystems into cultivated environments make emerge changes in soil carbon dynamics, often leading to a decline in soil organic carbon content. Therefore, our aim was to use data mining techniques to generate predictive models for the effect of soil use on carbon stock in natural and transformed areas of Archaeological Black Lands. We carried out our experiment in Manicoré and Apuí, Amazonas State, Brazil. After field data collection and laboratory analysis, we obtained a set of data consisting of 21 attributes, 20 predictive attributes consisting of 13 soil physical attributes, 6 soil chemical attributes, 1 soil use related attribute, and 1 response variable, referring to soil carbon stock (SCS), which is the classification target. Due to the large number of attributes, we performed a selection procedure to eliminate attributes of low correlation to the response variable. For data classification, we used the binary induction technique of the decision tree through software Weka 3.8. The results obtained showed that for the depth of 0.00-0.05 and 0.05-0.10 m, the best selected subset was determined using the Wrapper method for attribute selection. In the depth of 0.00-0.05 m, we generated a model of 79% accuracy containing only six rules, including, as the most important classification attribute was soil use. For the depth of 0.05-0.10 m, we generated an eight-rule decision tree of 74% accuracy including sand as the most important attribute. In this context, we highlight the Wrapper method efficiency to select subsets of predictive attributes, capable to generate more understandable decision trees, using a smaller number of attributes in the classification process, making it faster and with a lower computational cost. In addition, data mining techniques were efficient at providing predictive models capable to assist the decision-making process on possible management practices with the potential to conserve or increase soil carbon stock in archeological black lands.
653 $aAnthropic soils
653 $aÁrvore de decisão
653 $aData mining techniques
653 $aDecision tree
653 $aMineração de dados
653 $aSoil management system
700 1 $aSOARES, M. D. R.
700 1 $aOLIVEIRA, I. N. de
700 1 $aFARHATE, C. V. V.
700 1 $aSOUZA, Z. M. de
700 1 $aOLIVEIRA, S. R. de M.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
URL |
Voltar
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Registro Completo
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Biblioteca(s): |
Embrapa Caprinos e Ovinos. |
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Data corrente: |
12/04/2024 |
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Data da última atualização: |
15/04/2024 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 3 |
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Autoria: |
CÂNDIDO, M. J. D.; MARANHÃO, S. R.; ANGERER, J. P.; CAVALCANTE, A. C. R.; SILVA., V. J. da; SILVA, R. G. da. |
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Afiliação: |
MAGNO JOSÉ DUARTE CÂNDIDO, UNIVERSIDADE FEDERAL DO CEARÁ; SAMUEL ROCHA MARANHÃO, INSTITUTO FEDERAL DE EDUCAÇÃO, CIÊNCIA E TECNOLOGIA DO CEARÁ; JAY PETER ANGERER, TEXAS A&M AGRILIFE RESEARCH; ANA CLARA RODRIGUES CAVALCANTE, CNPC; VALDSON JOSÉ DA SILVA, UNIVERSIDADE FEDERAL DE PERNAMBUCO; RODRIGO GREGÓRIO DA SILVA, INSTITUTO FEDERAL DE EDUCAÇÃO, CIÊNCIA E TECNOLOGIA DO CEARÁ. |
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Título: |
Stabilized Forage Guarantee System: defi ning a forage storage capacity to stabilize livestock production in vulnerable ecosystems. |
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Ano de publicação: |
2024 |
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Fonte/Imprenta: |
Revista Ciência Agronômica, v. 55, e20218317, 2024. |
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Idioma: |
Inglês |
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Conteúdo: |
Abstract: Livestock production in semi-arid areas has been unpredictable due to climate variability, mainly rainfall. This study aims to simulate rangeland production variability aff ected by rainfall over time and relate it to an adjusted carrying capacity, using forage stock to maximize the potential of production of the system to a specifi c guarantee level. Regression analysis of forage biomass against rainfall was performed for ecological sites in the Brazilian semi-arid region to generate probability distribution curves for the historical rainfall for each location using Monte Carlo approach. Forage biomass variability estimated over time was used as input to the model. The system optimizes forage use at a sustainable stocking rate and uses forage surpluses in good years to fi ll defi cits during adverse years, due to a certain level of guarantee. As a rule, smallholder farmers would need to maintain a storage of around 1,500 kg ha-1 of DM of forage to maintain an adjusted carrying capacity of 0.11 animal units ha-1, with a guarantee of 95% in the long term, stressing the forage storage capacity as a central component of the model. Since farm size infl uences forage production capacity and mainly forage stock capacity, recommendations to cope with this paradigm are suggested. Resumo: A produção pecuária em regiões semiáridas tem sido imprevisível devido à variabilidade climática, principalmente quanto as chuvas. Este estudo tem como objetivo simular a variabilidade da produção de pastagens afetadas pela chuva ao longo do tempo e relacioná-la a uma capacidade de suporte ajustada, utilizando um estoque de forragem para maximizar o potencial de produção do sistema para um determinado nível de garantia. Análises de regressão da biomassa da forragem em relação à chuva foram realizadas para sítios ecológicos no Semiárido Brasileiro para gerar curvas de distribuição de probabilidade para a precipitação histórica para cada local usando a abordagem de Monte Carlo. A variabilidade da biomassa da forragem estimada ao longo do tempo foi usada como entrada para o modelo. O sistema otimiza o uso de forragem a uma taxa de lotação sustentável e usa sobras de forragem em anos bons para suprir déficits em anos adversos, dado um certo nível de garantia. Via de regra, os pequenos agricultores precisariam manter um armazenamento em torno de 1,500 kg ha-1 de MS de forragem para manter uma capacidade de carga ajustada de 0,11 unidades animais ha-1, com garantia de 95% no longo prazo, frisando a capacidade de armazenamento de forragem como componente central do modelo. Como o tamanho da fazenda influencia a capacidade de produção de forragem e, principalmente, do estoque de forragem, recomendações para lidar com este paradigma são sugeridas. MenosAbstract: Livestock production in semi-arid areas has been unpredictable due to climate variability, mainly rainfall. This study aims to simulate rangeland production variability aff ected by rainfall over time and relate it to an adjusted carrying capacity, using forage stock to maximize the potential of production of the system to a specifi c guarantee level. Regression analysis of forage biomass against rainfall was performed for ecological sites in the Brazilian semi-arid region to generate probability distribution curves for the historical rainfall for each location using Monte Carlo approach. Forage biomass variability estimated over time was used as input to the model. The system optimizes forage use at a sustainable stocking rate and uses forage surpluses in good years to fi ll defi cits during adverse years, due to a certain level of guarantee. As a rule, smallholder farmers would need to maintain a storage of around 1,500 kg ha-1 of DM of forage to maintain an adjusted carrying capacity of 0.11 animal units ha-1, with a guarantee of 95% in the long term, stressing the forage storage capacity as a central component of the model. Since farm size infl uences forage production capacity and mainly forage stock capacity, recommendations to cope with this paradigm are suggested. Resumo: A produção pecuária em regiões semiáridas tem sido imprevisível devido à variabilidade climática, principalmente quanto as chuvas. Este estudo tem como objetivo simular a variabilidade da ... Mostrar Tudo |
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Palavras-Chave: |
Forage supply; Guarantee concept; Modelagem de sistema de produção; Monte Carlo approach; Oferta de forragem; Semi-arid regions; Semiárido; Sustainable livestock production. |
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Thesagro: |
Biomassa; Estoque; Forragem. |
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Thesaurus NAL: |
Biomass production; Brazil; Rangelands; Semiarid soils. |
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Categoria do assunto: |
L Ciência Animal e Produtos de Origem Animal |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1163578/1/CNPC-2024-Stabilized-Forage-Guarantee.pdf
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Marc: |
LEADER 03899naa a2200361 a 4500
001 2163578
005 2024-04-15
008 2024 bl uuuu u00u1 u #d
100 1 $aCÂNDIDO, M. J. D.
245 $aStabilized Forage Guarantee System$bdefi ning a forage storage capacity to stabilize livestock production in vulnerable ecosystems.$h[electronic resource]
260 $c2024
520 $aAbstract: Livestock production in semi-arid areas has been unpredictable due to climate variability, mainly rainfall. This study aims to simulate rangeland production variability aff ected by rainfall over time and relate it to an adjusted carrying capacity, using forage stock to maximize the potential of production of the system to a specifi c guarantee level. Regression analysis of forage biomass against rainfall was performed for ecological sites in the Brazilian semi-arid region to generate probability distribution curves for the historical rainfall for each location using Monte Carlo approach. Forage biomass variability estimated over time was used as input to the model. The system optimizes forage use at a sustainable stocking rate and uses forage surpluses in good years to fi ll defi cits during adverse years, due to a certain level of guarantee. As a rule, smallholder farmers would need to maintain a storage of around 1,500 kg ha-1 of DM of forage to maintain an adjusted carrying capacity of 0.11 animal units ha-1, with a guarantee of 95% in the long term, stressing the forage storage capacity as a central component of the model. Since farm size infl uences forage production capacity and mainly forage stock capacity, recommendations to cope with this paradigm are suggested. Resumo: A produção pecuária em regiões semiáridas tem sido imprevisível devido à variabilidade climática, principalmente quanto as chuvas. Este estudo tem como objetivo simular a variabilidade da produção de pastagens afetadas pela chuva ao longo do tempo e relacioná-la a uma capacidade de suporte ajustada, utilizando um estoque de forragem para maximizar o potencial de produção do sistema para um determinado nível de garantia. Análises de regressão da biomassa da forragem em relação à chuva foram realizadas para sítios ecológicos no Semiárido Brasileiro para gerar curvas de distribuição de probabilidade para a precipitação histórica para cada local usando a abordagem de Monte Carlo. A variabilidade da biomassa da forragem estimada ao longo do tempo foi usada como entrada para o modelo. O sistema otimiza o uso de forragem a uma taxa de lotação sustentável e usa sobras de forragem em anos bons para suprir déficits em anos adversos, dado um certo nível de garantia. Via de regra, os pequenos agricultores precisariam manter um armazenamento em torno de 1,500 kg ha-1 de MS de forragem para manter uma capacidade de carga ajustada de 0,11 unidades animais ha-1, com garantia de 95% no longo prazo, frisando a capacidade de armazenamento de forragem como componente central do modelo. Como o tamanho da fazenda influencia a capacidade de produção de forragem e, principalmente, do estoque de forragem, recomendações para lidar com este paradigma são sugeridas.
650 $aBiomass production
650 $aBrazil
650 $aRangelands
650 $aSemiarid soils
650 $aBiomassa
650 $aEstoque
650 $aForragem
653 $aForage supply
653 $aGuarantee concept
653 $aModelagem de sistema de produção
653 $aMonte Carlo approach
653 $aOferta de forragem
653 $aSemi-arid regions
653 $aSemiárido
653 $aSustainable livestock production
700 1 $aMARANHÃO, S. R.
700 1 $aANGERER, J. P.
700 1 $aCAVALCANTE, A. C. R.
700 1 $aSILVA., V. J. da
700 1 $aSILVA, R. G. da
773 $tRevista Ciência Agronômica$gv. 55, e20218317, 2024.
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