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Registro Completo |
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Biblioteca(s): |
Embrapa Meio Ambiente. |
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Data corrente: |
24/11/2022 |
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Data da última atualização: |
25/11/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
MACIEL, V. G.; NOVAES, R. M. L.; BRANDÃO, M.; CAVALETT, O.; PAZIANOTTO, R. A. A.; GAROFALO, D. F. T.; MATSUURA, M. I. da S. F. |
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Afiliação: |
VINÍCIUS GONÇALVES MACIEL; RENAN MILAGRES LAGE NOVAES, CNPMA; MIGUEL BRANDÃO, KTH ROYAL INSTITUTE OF TECHNOLOGY; OTÁVIO CAVALETT, NORWEGIAN UNIVERSITY OF SCIENCE AND TECHNOLOGY; RICARDO ANTONIO ALMEIDA PAZIANOTTO, CNPMA; DANILO FRANCISCO TROVO GAROFALO; MARILIA IEDA DA S F MATSUURA, CNPMA. |
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Título: |
Towards a non-ambiguous view of the amortization period for quantifying direct land-use change in LCA. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
The International Journal of Life Cycle Assessment, v. 27, n. 12, p. 1299-1315, 2022. |
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ISSN: |
1614-7502 |
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Idioma: |
Inglês |
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Conteúdo: |
Abstract: Purpose: To clarify the concept of the amortization period (20-year factor) associated with direct land-use change (dLUC) accounting, discuss its main inconsistencies, and propose improvements. The current practice is to divide (amortize) the estimated emissions associated with dLUC that has occurred over the last 20 years by another 20 years. Both periods are referred ambiguously as amortization period. Issues arise when considering them as a single temporal aspect (TA) that cannot fully represent the complexity of diverse research and policy contexts. Methods: First, a systematic review was conducted to understand the 20-year amortization history and concepts and discuss its inconsistencies. Based on the review results, we propose the adoption of two distinct TAs, decomposed from the amortization period. Then, we performed a sensitivity analysis by estimating carbon emissions due to dLUC in six land uses in Brazil: soybean, maize, sugarcane, pasture, planted forest, and mango. Results and discussion: The literature review shows that several strategies have emerged to reduce or avoid adopting the amortization period. However, most of these proposals are based on complex approaches focusing on alternatives associated with the life cycle impact assessment stage. We found that the commonly adopted amortization period has an ambiguous nature that could be explored at the life cycle inventory analysis stage. Thus, we argue that there are two distinct TAs linked to amortization in dLUC: (i) the inventory period adopted to account for land-use changes; and (ii) the period over which carbon emissions are annualized. These temporal aspects were named here the LUC-inventory period (IP) and the LUC-amortization period (AP), for clarification purposes. The sensitivity analysis showed that different values of IP and AP drastically change the emissions results due to dLUC in Brazil for different crops and land uses. Conclusion: We advocate that the amortization period should be decomposed into two TAs: LUC-inventory period and the LUC-amortization period. They affect how the carbon debt incurred by expanding agricultural land is accounted for and amortized over a given period-of-time. Therefore, to ensure regulatory compliance, we argued that these proposed TAs should be explicitly defined, based on three possibilities, depending on the goal and context of LCA studies, such as (i) fixed values set in standards and norms; (ii) IPCC's 20-year defaults; and (iii) customized IP and AP values based on the study's specificities. MenosAbstract: Purpose: To clarify the concept of the amortization period (20-year factor) associated with direct land-use change (dLUC) accounting, discuss its main inconsistencies, and propose improvements. The current practice is to divide (amortize) the estimated emissions associated with dLUC that has occurred over the last 20 years by another 20 years. Both periods are referred ambiguously as amortization period. Issues arise when considering them as a single temporal aspect (TA) that cannot fully represent the complexity of diverse research and policy contexts. Methods: First, a systematic review was conducted to understand the 20-year amortization history and concepts and discuss its inconsistencies. Based on the review results, we propose the adoption of two distinct TAs, decomposed from the amortization period. Then, we performed a sensitivity analysis by estimating carbon emissions due to dLUC in six land uses in Brazil: soybean, maize, sugarcane, pasture, planted forest, and mango. Results and discussion: The literature review shows that several strategies have emerged to reduce or avoid adopting the amortization period. However, most of these proposals are based on complex approaches focusing on alternatives associated with the life cycle impact assessment stage. We found that the commonly adopted amortization period has an ambiguous nature that could be explored at the life cycle inventory analysis stage. Thus, we argue that there are two distinct TAs linked to amor... Mostrar Tudo |
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Palavras-Chave: |
Land transformation; LUC; Temporal aspects; Temporal dependence. |
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Thesagro: |
Agricultura; Cana de Açúcar; Floresta; Manga; Milho; Pastagem; Soja; Uso da Terra. |
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Thesaurus Nal: |
Agriculture; Brazil; Carbon footprint; Corn; Greenhouse gas emissions; Land use change; Life cycle assessment; Pastures; Plantation forestry; Soybeans; Sugarcane. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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Marc: |
LEADER 03887naa a2200481 a 4500
001 2148673
005 2022-11-25
008 2022 bl uuuu u00u1 u #d
022 $a1614-7502
100 1 $aMACIEL, V. G.
245 $aTowards a non-ambiguous view of the amortization period for quantifying direct land-use change in LCA.$h[electronic resource]
260 $c2022
520 $aAbstract: Purpose: To clarify the concept of the amortization period (20-year factor) associated with direct land-use change (dLUC) accounting, discuss its main inconsistencies, and propose improvements. The current practice is to divide (amortize) the estimated emissions associated with dLUC that has occurred over the last 20 years by another 20 years. Both periods are referred ambiguously as amortization period. Issues arise when considering them as a single temporal aspect (TA) that cannot fully represent the complexity of diverse research and policy contexts. Methods: First, a systematic review was conducted to understand the 20-year amortization history and concepts and discuss its inconsistencies. Based on the review results, we propose the adoption of two distinct TAs, decomposed from the amortization period. Then, we performed a sensitivity analysis by estimating carbon emissions due to dLUC in six land uses in Brazil: soybean, maize, sugarcane, pasture, planted forest, and mango. Results and discussion: The literature review shows that several strategies have emerged to reduce or avoid adopting the amortization period. However, most of these proposals are based on complex approaches focusing on alternatives associated with the life cycle impact assessment stage. We found that the commonly adopted amortization period has an ambiguous nature that could be explored at the life cycle inventory analysis stage. Thus, we argue that there are two distinct TAs linked to amortization in dLUC: (i) the inventory period adopted to account for land-use changes; and (ii) the period over which carbon emissions are annualized. These temporal aspects were named here the LUC-inventory period (IP) and the LUC-amortization period (AP), for clarification purposes. The sensitivity analysis showed that different values of IP and AP drastically change the emissions results due to dLUC in Brazil for different crops and land uses. Conclusion: We advocate that the amortization period should be decomposed into two TAs: LUC-inventory period and the LUC-amortization period. They affect how the carbon debt incurred by expanding agricultural land is accounted for and amortized over a given period-of-time. Therefore, to ensure regulatory compliance, we argued that these proposed TAs should be explicitly defined, based on three possibilities, depending on the goal and context of LCA studies, such as (i) fixed values set in standards and norms; (ii) IPCC's 20-year defaults; and (iii) customized IP and AP values based on the study's specificities.
650 $aAgriculture
650 $aBrazil
650 $aCarbon footprint
650 $aCorn
650 $aGreenhouse gas emissions
650 $aLand use change
650 $aLife cycle assessment
650 $aPastures
650 $aPlantation forestry
650 $aSoybeans
650 $aSugarcane
650 $aAgricultura
650 $aCana de Açúcar
650 $aFloresta
650 $aManga
650 $aMilho
650 $aPastagem
650 $aSoja
650 $aUso da Terra
653 $aLand transformation
653 $aLUC
653 $aTemporal aspects
653 $aTemporal dependence
700 1 $aNOVAES, R. M. L.
700 1 $aBRANDÃO, M.
700 1 $aCAVALETT, O.
700 1 $aPAZIANOTTO, R. A. A.
700 1 $aGAROFALO, D. F. T.
700 1 $aMATSUURA, M. I. da S. F.
773 $tThe International Journal of Life Cycle Assessment$gv. 27, n. 12, p. 1299-1315, 2022.
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Embrapa Meio Ambiente (CNPMA) |
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Biblioteca(s): |
Embrapa Trigo. |
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Data corrente: |
11/12/2023 |
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Data da última atualização: |
11/12/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
B - 3 |
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Autoria: |
LAZZARETTI, A. T.; SCHNEIDER, V. R.; WIEST, R.; LAU, D.; FERNANDES, J. M. C.; FRAISSE, C. W.; CERBARO, V. A.; KARREI, M. Z. |
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Afiliação: |
ALEXANDRE TAGLIARI LAZZARETTI, Instituto Federal Sul-Riograndense; VINICIUS RAFAEL SCHNEIDER, Instituto Federal Sul-Riograndense; ROBERTO WIEST, Instituto Federal Sul-Riograndense; DOUGLAS LAU, CNPT; JOSE MAURICIO CUNHA FERNANDES, CNPT; CLYDE W. FRAISSE, Universidade da Flórida; VINÍCIUS ANDREI CERBARO, Universidade da Flórida; MAURÍCIO Z. KARREI, Universidade da Flórida. |
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Título: |
Implementação e comparação de técnicas de machine learning aplicadas à predição do desenvolvimento de populações de afídeos. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
Revista Brasileira de Computação Aplicada, v. 15, n. 3, p. 25-37, nov. 2023. |
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DOI: |
https://doi.org/10.5335/rbca.v15i3.13467 |
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Idioma: |
Português |
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Conteúdo: |
Resumo: Os insetos ao atingirem um determinado nível populacional podem causar danos às plantas, sendo considerados pragas. Afídeos ou pulgões apresentam um alto potencial biótico e podem causar diferentes tipos de dano às plantas. Fatores meteorológicos como precipitações, ventos e temperatura interferem no crescimento populacional destes insetos. Este trabalho aplicou diferentes técnicas de machine learning com o objetivo de verificar a correlação existente entre variáveis meteorológicas e a dinâmica populacional dos afídeos. Foram implementados 4 (quatro) modelos obtendo-se as acurácias de 11,4% para Regressão Linear; 26,4% para o modelo de Rede Neural Artificial; 29,3% para Árvore de decisão e 41,4% para random forest. Abstract: Insects have an important degree of collaboration for the maintenance of the ecosystem on the planet. However, after reaching a certain population level and causing damage to plants, some insects are considered as pests and represent a threat to agriculture. Aphids insects that has characteristics to reach this state as it has a high biotic potential and can cause different types of damage to plants. Climatic data as precipitation, winds and temperatures affect the population quantity of these insects. Therefore, this work proposes to apply different machine learning techniques with the objective to verify the existing correlation between climatic variables and the population dynamics of aphids. It can be concluded that variables such as precipitation, temperature, number of days when it rains in the week and climatic phenomena such as El niño and La niña have an influence on the aphid population. During the work, four models were developed in order to predict the population of these insects. The accuracy of the prediction model developed were 11.4% for Linear Regression; 26.4% for the Artificial Neural Network model; 29.3% for Decision Tree and 41.4% for Random Forest. MenosResumo: Os insetos ao atingirem um determinado nível populacional podem causar danos às plantas, sendo considerados pragas. Afídeos ou pulgões apresentam um alto potencial biótico e podem causar diferentes tipos de dano às plantas. Fatores meteorológicos como precipitações, ventos e temperatura interferem no crescimento populacional destes insetos. Este trabalho aplicou diferentes técnicas de machine learning com o objetivo de verificar a correlação existente entre variáveis meteorológicas e a dinâmica populacional dos afídeos. Foram implementados 4 (quatro) modelos obtendo-se as acurácias de 11,4% para Regressão Linear; 26,4% para o modelo de Rede Neural Artificial; 29,3% para Árvore de decisão e 41,4% para random forest. Abstract: Insects have an important degree of collaboration for the maintenance of the ecosystem on the planet. However, after reaching a certain population level and causing damage to plants, some insects are considered as pests and represent a threat to agriculture. Aphids insects that has characteristics to reach this state as it has a high biotic potential and can cause different types of damage to plants. Climatic data as precipitation, winds and temperatures affect the population quantity of these insects. Therefore, this work proposes to apply different machine learning techniques with the objective to verify the existing correlation between climatic variables and the population dynamics of aphids. It can be concluded that variables such as precipit... Mostrar Tudo |
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Palavras-Chave: |
Artificial neural networks; Árvore de decisão; Decision tree; Exploratory Data; Extração de conhecimento; Knowledge extraction; Linear Regression; Random Forest; Redes Neurais Artificiais. |
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Thesagro: |
Afídeo; Análise de Dados; Praga de Planta; Pulgão; Regressão Linear. |
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Thesaurus NAL: |
Plant pests; Plants (botany). |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1159411/1/Implementacao-e-comparacao-de-tecnicas-de-machine-learning-LAU.pdf
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Marc: |
LEADER 03226naa a2200409 a 4500
001 2159411
005 2023-12-11
008 2023 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.5335/rbca.v15i3.13467$2DOI
100 1 $aLAZZARETTI, A. T.
245 $aImplementação e comparação de técnicas de machine learning aplicadas à predição do desenvolvimento de populações de afídeos.$h[electronic resource]
260 $c2023
520 $aResumo: Os insetos ao atingirem um determinado nível populacional podem causar danos às plantas, sendo considerados pragas. Afídeos ou pulgões apresentam um alto potencial biótico e podem causar diferentes tipos de dano às plantas. Fatores meteorológicos como precipitações, ventos e temperatura interferem no crescimento populacional destes insetos. Este trabalho aplicou diferentes técnicas de machine learning com o objetivo de verificar a correlação existente entre variáveis meteorológicas e a dinâmica populacional dos afídeos. Foram implementados 4 (quatro) modelos obtendo-se as acurácias de 11,4% para Regressão Linear; 26,4% para o modelo de Rede Neural Artificial; 29,3% para Árvore de decisão e 41,4% para random forest. Abstract: Insects have an important degree of collaboration for the maintenance of the ecosystem on the planet. However, after reaching a certain population level and causing damage to plants, some insects are considered as pests and represent a threat to agriculture. Aphids insects that has characteristics to reach this state as it has a high biotic potential and can cause different types of damage to plants. Climatic data as precipitation, winds and temperatures affect the population quantity of these insects. Therefore, this work proposes to apply different machine learning techniques with the objective to verify the existing correlation between climatic variables and the population dynamics of aphids. It can be concluded that variables such as precipitation, temperature, number of days when it rains in the week and climatic phenomena such as El niño and La niña have an influence on the aphid population. During the work, four models were developed in order to predict the population of these insects. The accuracy of the prediction model developed were 11.4% for Linear Regression; 26.4% for the Artificial Neural Network model; 29.3% for Decision Tree and 41.4% for Random Forest.
650 $aPlant pests
650 $aPlants (botany)
650 $aAfídeo
650 $aAnálise de Dados
650 $aPraga de Planta
650 $aPulgão
650 $aRegressão Linear
653 $aArtificial neural networks
653 $aÁrvore de decisão
653 $aDecision tree
653 $aExploratory Data
653 $aExtração de conhecimento
653 $aKnowledge extraction
653 $aLinear Regression
653 $aRandom Forest
653 $aRedes Neurais Artificiais
700 1 $aSCHNEIDER, V. R.
700 1 $aWIEST, R.
700 1 $aLAU, D.
700 1 $aFERNANDES, J. M. C.
700 1 $aFRAISSE, C. W.
700 1 $aCERBARO, V. A.
700 1 $aKARREI, M. Z.
773 $tRevista Brasileira de Computação Aplicada$gv. 15, n. 3, p. 25-37, nov. 2023.
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