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| 1. |  | LIMA, R. N. de; LIMA, P. de O.; AROEIRA, L. J. M.; MIRANDA, M. V. F. G. de; LOPES, K. T. de L.; DIÓGENES, G. V.; PEREIRA, M. I. B.; SOUZA, I. T. do N.; ROSSATO, C. H. Desempenho de bezerros aleitados com soro de queijo em associação ao colostro. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 47, n. 8, p. 1174-1180, ago. 2012. Título em inglês: Performance of calves fed cheese whey associated with colostrum.| Biblioteca(s): Embrapa Unidades Centrais. |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Solos. Para informações adicionais entre em contato com cnps.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Solos. |
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Data corrente: |
07/07/2015 |
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Data da última atualização: |
19/05/2020 |
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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: |
GRUNWALD, S.; VASQUES, G. M.; RIVERO, R. G. |
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Afiliação: |
SABINE GRUNWALD, UNIVERSITY OF FLORIDA; GUSTAVO DE MATTOS VASQUES, CNPS; ROSANNA G. RIVERO, UNIVERSITY OF GEORGIA. |
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Título: |
Fusion of soil and remote sensing data to model soil properties. |
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Ano de publicação: |
2015 |
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Fonte/Imprenta: |
Advances in Agronomy, v. 131, p. 1-109, 2015. |
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DOI: |
10.1016/bs.agron.2014.12.004 |
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Idioma: |
Inglês |
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Conteúdo: |
Grand global challenges of our time, such as food security and soil security, cannot be met without up-to-date, high-quality, high-resolution, spatiotemporal, and continuous soil and environmental data that characterize soil ecosystems. At local and regional scales, accurate and precise soil assessment is critical for management, soil health, and sustainability. This article presents integration pathways fusing lab and field-based soil measurements, proximal and remote sensor data, environmental covariates, and/or methods within the framework of the Meta Soil Model which is poised to extend contemporary soil applications. The STEP-AWBH model allows to quantify soil-environmental covariates (S: soil, T: topography, E: ecology, P: parent material, A: atmosphere, W: water, B: biota, H: human factors) of which numerous can be sensed. We present an in-depth overview of proximal and remote sensor technologies that are used in the realm of digital soil assessment. Specific attention is given to the fusion process of (1) proximal, (2) proximal/remote, and (3) remote sensors to directly sense or predict soil properties. We highlight the promises and perils of sensor-derived proxies that allow inferences on soil properties and their change. From our review it is evident that there is no such single sensor or method that fits all soil applications. In many studies the fusion/integration of data and methods enhance the capabilities to assess specific soil properties. We critically contrast the benefits and constraints of proximal and remote sensing, fusion of soil-environmental data, and integration pathways to mash data and methods into complex soil assessments. MenosGrand global challenges of our time, such as food security and soil security, cannot be met without up-to-date, high-quality, high-resolution, spatiotemporal, and continuous soil and environmental data that characterize soil ecosystems. At local and regional scales, accurate and precise soil assessment is critical for management, soil health, and sustainability. This article presents integration pathways fusing lab and field-based soil measurements, proximal and remote sensor data, environmental covariates, and/or methods within the framework of the Meta Soil Model which is poised to extend contemporary soil applications. The STEP-AWBH model allows to quantify soil-environmental covariates (S: soil, T: topography, E: ecology, P: parent material, A: atmosphere, W: water, B: biota, H: human factors) of which numerous can be sensed. We present an in-depth overview of proximal and remote sensor technologies that are used in the realm of digital soil assessment. Specific attention is given to the fusion process of (1) proximal, (2) proximal/remote, and (3) remote sensors to directly sense or predict soil properties. We highlight the promises and perils of sensor-derived proxies that allow inferences on soil properties and their change. From our review it is evident that there is no such single sensor or method that fits all soil applications. In many studies the fusion/integration of data and methods enhance the capabilities to assess specific soil properties. We critically contr... Mostrar Tudo |
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Palavras-Chave: |
Covariáveis ambientais; Detecção proximal; Fusão de dados; Mapeamento digital do solo; Modelagem digital do solo; Modelo meta do solo; Pedometria; Propriedades do solo; Sensores; Vias de integração. |
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Thesagro: |
Mapa; Sensoriamento remoto. |
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Thesaurus NAL: |
Remote sensing; Soil surveys. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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Marc: |
LEADER 02618naa a2200325 a 4500
001 2019476
005 2020-05-19
008 2015 bl uuuu u00u1 u #d
024 7 $a10.1016/bs.agron.2014.12.004$2DOI
100 1 $aGRUNWALD, S.
245 $aFusion of soil and remote sensing data to model soil properties.$h[electronic resource]
260 $c2015
520 $aGrand global challenges of our time, such as food security and soil security, cannot be met without up-to-date, high-quality, high-resolution, spatiotemporal, and continuous soil and environmental data that characterize soil ecosystems. At local and regional scales, accurate and precise soil assessment is critical for management, soil health, and sustainability. This article presents integration pathways fusing lab and field-based soil measurements, proximal and remote sensor data, environmental covariates, and/or methods within the framework of the Meta Soil Model which is poised to extend contemporary soil applications. The STEP-AWBH model allows to quantify soil-environmental covariates (S: soil, T: topography, E: ecology, P: parent material, A: atmosphere, W: water, B: biota, H: human factors) of which numerous can be sensed. We present an in-depth overview of proximal and remote sensor technologies that are used in the realm of digital soil assessment. Specific attention is given to the fusion process of (1) proximal, (2) proximal/remote, and (3) remote sensors to directly sense or predict soil properties. We highlight the promises and perils of sensor-derived proxies that allow inferences on soil properties and their change. From our review it is evident that there is no such single sensor or method that fits all soil applications. In many studies the fusion/integration of data and methods enhance the capabilities to assess specific soil properties. We critically contrast the benefits and constraints of proximal and remote sensing, fusion of soil-environmental data, and integration pathways to mash data and methods into complex soil assessments.
650 $aRemote sensing
650 $aSoil surveys
650 $aMapa
650 $aSensoriamento remoto
653 $aCovariáveis ambientais
653 $aDetecção proximal
653 $aFusão de dados
653 $aMapeamento digital do solo
653 $aModelagem digital do solo
653 $aModelo meta do solo
653 $aPedometria
653 $aPropriedades do solo
653 $aSensores
653 $aVias de integração
700 1 $aVASQUES, G. M.
700 1 $aRIVERO, R. G.
773 $tAdvances in Agronomy$gv. 131, p. 1-109, 2015.
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