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| 3. |  | HENTONG, Q.; JIAJI, G.; HONG, J.; HUEIZZHONG, C.; SANQI, X.; XIAOFENG, Y. The breeding of hybrid watermelon "Ju Bao n.1". Acta Horticulturae Sinica, v.19, n.1, p.93-97, 1992.| Biblioteca(s): Embrapa Meio Norte / UEP-Parnaíba. |
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| 4. | | HONG, J.; KIM, D.; CHO, K.; SA, S.; CHOI, S.; KIM, Y.; PARK, J.; SCHMIDT, G. S.; DAVIS, M. E.; CHUNG, H. Effects of genetic variants for the swine FABP3, HMGA1, MC4R, IGF2, and FABP4 genes on fatty acid composition. Meat Science, v. 110, p. 46-51, 2015.| Biblioteca(s): Embrapa Suínos e Aves. |
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| 5. | | KUHN, J.; ADKINS, S.; AGWANDA, B. R.; KUBRUSLI, R. A.; ALKHOVSKY, S. V.; AMARASINGHE, G. K.; TATJANA, A.-Z.; AYLLÓN, M. A.; BAHL, J.; BALKEMA BUSCHMANN, A.; BALLINGER, M. J.; BASLER, C. F.; BAVARI, S.; BEER, M.; BEJERMAN, N.; BENNETT, A. J.; DBENTE, D. A.; BERGERON, É.; BIRD, B. H.; BLAIR, C. D.; BLASDELL, K. R.; BLYSTAD, D. R.; BOJKO, J.; BORTH, W. B.; BRADFUTE, S.; BREYTA, R.; BRIESE, T.; BROWN, P. A.; BROWN, J. K.; BUCHHOLZ, U. J.; BUCHMEIER, M. J.; ABUKREYEV, A.; BURT, F.; BÜTTNER, C.; CALISHER, C. H.; CAO, M.; CASAS, I.; CHANDRAN, K.; CHARREL, R. N.; CHENG, Q.; CHIAKI, Y.; CHIAPELLO, M.; CHOI, I. R.; CIUFO, M.; CLEGG, J. C. S.; CROZIER, I.; BÓ, E. D.; DE LA TORRE, J. C.; LAMBALLERIE, X. de; SWART, R. L. de; DEBAT, H.; DHEILLY, N. M.; DI CICCO, E.; DI PAOLA, N.; DI SERIO, F.; DIETZGEN, R. G.; DIGIARO, M.; DOLNIK, O.; DREBOT, M. A.; DREXLER, J. F.; DUNDON, W. G.; DUPREX, W. P.; DÜRRWALD, R.; DYE, J. M.; EASTON, A. J.; EBIHARA, H.; ELBEAINO, T.; ERGÜNAY, K.; FERGUSON, H. W.; FOOKS, A. R.; FORGIA, M.; FORMENTY, P. B. H.; FRÁNOVÁ, J.; ASTUA, J. de F.; FU, J.; FÜRL, S.; ZACHERT, S. G.; GĀO, G. F.; GARCÍA, M. L.; SASTRE, A. G.; GARRISON, A. R.; GASKIN, T.; GONZALE, J. P. J.; GRIFTHS, A.; GOLDBERG, T. L.; GROSCHUP, M. H.; GÜNTHER, S.; HALL, R. A.; HAMMOND, J.; HAN, O.; HEPOJOKI, J.; HEWSON, R.; HONG, J.; HONG, N.; HORIE, M.; HU, J. S.; HU, T.; HUGHES, H. R.; HÜTTNER, F.; HYNDMAN, T. H.; ILYAS, M.; JALKANEN, R.; JIĀNG, D.; JONSON, G. J.; JUNGLEN, S.; KADONO, F.; KAUKINEN, K. H.; KAWATE, M.; KLEMPA, B.; KLINGSTRÖM, J.; KOBINGER, G.; KOLONIUK, I.; KONDŌ, H.; KOONIN, V.; KRUPOVIC, M.; KUBOTA, K.; KURATH, G.; LAENEN, L.; LAMBERT, A. J.; LANGEVIN, S. L.; LEE, B.; LEFKOWITZ, E. J.; LI, L.; LEROY, E. M.; LI, S.; LÍ, J.; LIU, H.; LUKASHEVICH, I. S.; MAES, P.; SOUZA, W. M. de; MARKLEWITZ, M.; MARSHALL, S. H.; MARZANO, S. L.; MASSART, S.; MCCAULEY, J. W.; MELZER, M.; MILLER, K. M.; MING, T. J.; MIRAZIMI, A.; MORDECAI, G. J.; MÜHLBACH, H. P.; MÜHLBERGER, E.; NAIDU, R.; NATSUAKI, T.; NAVARRO, J. A.; NETESOV, S. Y V.; NEUMANN, G.; NOWOTNY, N.; NUNES, M. R. T.; VELARDE, A. O.; PALACIOS, G.; PALLÁS, V.; PÁLYI, B.; PAPA, A.; PARASKEVOPOULOU, S.; PARK, A. C.; PARRISH, C. R.; PATTERSON, D. A.; CORRÊA, A. P.; PAWESKA, J. T.; PAYNE, S.; PERACCHIO, C.; PÉREZ, D. R.; POSTLER, T. S.; QI, L.; RADOSHITZKY, S. R.; RESENDE, R. O.; REYES, C. A.; RIMA, B. K.; LUNA, G. R.; ROMANOWSKI, V.; ROTA, P.; RUBBENSTROTH, D.; RUBINO, L.; RUNSTADLER, J. A.; SABANADZOVIC, S.; SALL, A. A.; SALVATO, M. S.; SANG, R.; SASAYA, T.; SCHULZE, A. D.; SCHWEMMLE, M.; SHI, M.; SHÍ, X.; SHIMOMOTO, Y.; SHIRAKO, Y.; SIDDELL, S. G.; SIMMONDS, P.; SIRONI, M.; SMAGGHE, G.; SMITHER, S.; SONG, J. W.; SPANN, K.; SPENGLER, J. R.; STENGLEIN, M. D.; STONE, D. M.; SUGANO, J.; TABATA, A.; TAKADA, A.; TAKEUCHI, S.; TCHOUASSI, D. P.; TEFER, A.; TESH, R. B.; THORNBURG, N. J.; TOMITAKA, Y.; TOMONAGA, K.; TORDO, N.; TORTO, B.; TOWNER, J. S.; TSUDA, S.; TU, C.; TURINA, M.; TZANETAKIS, I. E.; UCHIDA, J.; USUGI, T.; VAIRA, A. M.; VALLINO, M.; VAN DEN HOOGEN, B.; VARSANI, A.; VASILAKIS, N.; VERBEEK, M.; BARGEN, S. V.; WADA, J.; WAHL, V.; WALKER, P. J.; WANG, L. F.; WANG, G.; WANG, Y.; WANG, Y.; WAQAS, M.; WÈI, T.; WEN, S.; WHITFELD, A. E.; WILLIAMS, J. V.; WOLF, Y. I.; WU, J.; XU, L.; YANAGISAWA, H.; YANG, C.; YANG, Z.; ZERBINI, F. M.; ZHAI, L.; ZHANG, Y. Z.; ZHANG, O.; ZHANG, J.; ZHANG, Z.; ZHOU, X. 2021 Taxonomic update of phylum Negarnaviricota (Riboviria: Orthornavirae), including the large orders Bunyavirales and Mononegavirale. Archives of Virology, August, 2021.| Biblioteca(s): Embrapa Mandioca e Fruticultura. |
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Registro Completo
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Biblioteca(s): |
Embrapa Solos. |
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Data corrente: |
12/08/2015 |
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Data da última atualização: |
28/01/2016 |
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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: |
HONG, J.; GRUNWALD, S.; VASQUES, G. M. |
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Afiliação: |
JINSEOK HONG, EAST TENNESSEE STATE UNIVERSITY; SABINE GRUNWALD, UNIVERSITY OF FLORIDA; GUSTAVO DE MATTOS VASQUES, CNPS. |
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Título: |
Soil phosphorus landscape models for precision soil conservation. |
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Ano de publicação: |
2015 |
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Fonte/Imprenta: |
Journal of Environmental Quality, Madison, v. 44, n. 3, p. 739-753, 2015. |
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DOI: |
10.2134/jeq2014.09.0379 |
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Idioma: |
Inglês |
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Conteúdo: |
Phosphorus (P) enrichment in soils has been documented in the Santa Fe River watershed (SFRW, 3585 km2) in north-central Florida. Yet the environmental factors that control P distribution in soils across the landscape, with potential contribution to water quality impairment, are not well understood. The main goal of this study was to develop soil-landscape P models to support a "precision soil conservation" approach combining fine-scale (i.e., site-specific) and coarse-scale (i.e., watershed-extent) assessment of soil P. The specific objectives were to: (i) identify those environmental properties that impart the most control on the spatial distribution of soil Mehlich-1 extracted P (MP) in the SFRW; (ii) model the spatial patterns of soil MP using geostatistical methods; and (iii) assess model quality using independent validation samples. Soil MP data at 137 sites were fused with spatially explicit environmental covariates to develop soil MP prediction models using univariate (lognormal kriging, LNK) and multivariate methods (regression kriging, RK, and cokriging, CK). Incorporation of exhaustive environmental data into multivariate models (RK and CK) improved the prediction of soil MP in the SFRW compared with the univariate model (LNK), which relies solely on soil measurements. Among all tested environmental covariates, land use and vegetation related properties (topsoil) and geologic data (subsoil) showed the largest predictive power to build inferential models for soil MP. Findings from this study contribute to a better understanding of spatially explicit interactions between soil P and other environmental variables, facilitating improved land resource management while minimizing adverse risks to the environment. MenosPhosphorus (P) enrichment in soils has been documented in the Santa Fe River watershed (SFRW, 3585 km2) in north-central Florida. Yet the environmental factors that control P distribution in soils across the landscape, with potential contribution to water quality impairment, are not well understood. The main goal of this study was to develop soil-landscape P models to support a "precision soil conservation" approach combining fine-scale (i.e., site-specific) and coarse-scale (i.e., watershed-extent) assessment of soil P. The specific objectives were to: (i) identify those environmental properties that impart the most control on the spatial distribution of soil Mehlich-1 extracted P (MP) in the SFRW; (ii) model the spatial patterns of soil MP using geostatistical methods; and (iii) assess model quality using independent validation samples. Soil MP data at 137 sites were fused with spatially explicit environmental covariates to develop soil MP prediction models using univariate (lognormal kriging, LNK) and multivariate methods (regression kriging, RK, and cokriging, CK). Incorporation of exhaustive environmental data into multivariate models (RK and CK) improved the prediction of soil MP in the SFRW compared with the univariate model (LNK), which relies solely on soil measurements. Among all tested environmental covariates, land use and vegetation related properties (topsoil) and geologic data (subsoil) showed the largest predictive power to build inferential models for soil M... Mostrar Tudo |
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Palavras-Chave: |
Modelo de precisão. |
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Thesagro: |
Conservação do solo; Fósforo. |
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Thesaurus NAL: |
Phosphorus; Precision. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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Marc: |
LEADER 02383naa a2200217 a 4500
001 2021794
005 2016-01-28
008 2015 bl uuuu u00u1 u #d
024 7 $a10.2134/jeq2014.09.0379$2DOI
100 1 $aHONG, J.
245 $aSoil phosphorus landscape models for precision soil conservation.$h[electronic resource]
260 $c2015
520 $aPhosphorus (P) enrichment in soils has been documented in the Santa Fe River watershed (SFRW, 3585 km2) in north-central Florida. Yet the environmental factors that control P distribution in soils across the landscape, with potential contribution to water quality impairment, are not well understood. The main goal of this study was to develop soil-landscape P models to support a "precision soil conservation" approach combining fine-scale (i.e., site-specific) and coarse-scale (i.e., watershed-extent) assessment of soil P. The specific objectives were to: (i) identify those environmental properties that impart the most control on the spatial distribution of soil Mehlich-1 extracted P (MP) in the SFRW; (ii) model the spatial patterns of soil MP using geostatistical methods; and (iii) assess model quality using independent validation samples. Soil MP data at 137 sites were fused with spatially explicit environmental covariates to develop soil MP prediction models using univariate (lognormal kriging, LNK) and multivariate methods (regression kriging, RK, and cokriging, CK). Incorporation of exhaustive environmental data into multivariate models (RK and CK) improved the prediction of soil MP in the SFRW compared with the univariate model (LNK), which relies solely on soil measurements. Among all tested environmental covariates, land use and vegetation related properties (topsoil) and geologic data (subsoil) showed the largest predictive power to build inferential models for soil MP. Findings from this study contribute to a better understanding of spatially explicit interactions between soil P and other environmental variables, facilitating improved land resource management while minimizing adverse risks to the environment.
650 $aPhosphorus
650 $aPrecision
650 $aConservação do solo
650 $aFósforo
653 $aModelo de precisão
700 1 $aGRUNWALD, S.
700 1 $aVASQUES, G. M.
773 $tJournal of Environmental Quality, Madison$gv. 44, n. 3, p. 739-753, 2015.
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