| |
|
|
|
Registro Completo |
|
Biblioteca(s): |
Embrapa Territorial. |
|
Data corrente: |
15/09/2014 |
|
Data da última atualização: |
15/09/2014 |
|
Tipo da produção científica: |
Artigo em Periódico Indexado |
|
Autoria: |
LU, D.; LI, G.; VALLADARES, G. S.; BATISTELLA, M. |
|
Afiliação: |
DENGSHENG LU, INDIANA UNIVERSITY; G. LI, INDIANA STATE UNIVERSITY; GUSTAVO S. VALLADARES, CNPM; MATEUS BATISTELLA, CNPM. |
|
Título: |
Mapping soil erosion risk in Rondônia, Brazilian Amazonia: using rusle, remote sensing and GIS. |
|
Ano de publicação: |
2004 |
|
Fonte/Imprenta: |
Land Degradation & Development, v. 15, p. 499-512, 2004. |
|
DOI: |
10.1002/ldr.634 |
|
Idioma: |
Português |
|
Conteúdo: |
This article discusses research in which the authors applied the Revised Universal Soil Loss Equation (RUSLE), remote sensing, and geographical information system (GIS) to the maping of soil erosion risk in Brazilian Amazonia. Soil map and soil survey data were used to develop the soil erodibility factor (K), and a digital elevation model image was used to generate the topographic factor (LS). The cover-management factor (C) was developed based on vegetation, shade, and soil fraction images derived from spectral mixture analysis of a Landsat Enhanced Thematic Mapper Plus image. Assuming the same climatic conditions and no support practice in the study area, the rainfall?runoff erosivity (R) and the support practice (P) factors were not used. The majority of the study area has K values of less than 0.2, LS values of less than 2.5, and C values of less than 0.25. A soil erosion risk map with five classes (very low, low, medium, medium-high, and high) was produced based on the simplified RUSLE within the GIS environment, and was linked to land use and land cover (LULC) image to explore relationships between soil erosion risk and LULC distribution. The results indicate that most successional and mature forests are in very low and low erosion risk areas, while agroforestry and pasture are usually associated with medium to high risk areas. This research implies that remote sensing and GIS provide promising tools for evaluating and mapping soil erosion risk in Amazonia. |
|
Palavras-Chave: |
Brazilian Amazonia; GIS; RUSLE; Soil erosion risk. |
|
Thesaurus Nal: |
Remote sensing. |
|
Categoria do assunto: |
-- |
|
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/108416/1/4024.pdf
|
|
Marc: |
LEADER 02146naa a2200229 a 4500
001 1994981
005 2014-09-15
008 2004 bl uuuu u00u1 u #d
024 7 $a10.1002/ldr.634$2DOI
100 1 $aLU, D.
245 $aMapping soil erosion risk in Rondônia, Brazilian Amazonia$busing rusle, remote sensing and GIS.$h[electronic resource]
260 $c2004
520 $aThis article discusses research in which the authors applied the Revised Universal Soil Loss Equation (RUSLE), remote sensing, and geographical information system (GIS) to the maping of soil erosion risk in Brazilian Amazonia. Soil map and soil survey data were used to develop the soil erodibility factor (K), and a digital elevation model image was used to generate the topographic factor (LS). The cover-management factor (C) was developed based on vegetation, shade, and soil fraction images derived from spectral mixture analysis of a Landsat Enhanced Thematic Mapper Plus image. Assuming the same climatic conditions and no support practice in the study area, the rainfall?runoff erosivity (R) and the support practice (P) factors were not used. The majority of the study area has K values of less than 0.2, LS values of less than 2.5, and C values of less than 0.25. A soil erosion risk map with five classes (very low, low, medium, medium-high, and high) was produced based on the simplified RUSLE within the GIS environment, and was linked to land use and land cover (LULC) image to explore relationships between soil erosion risk and LULC distribution. The results indicate that most successional and mature forests are in very low and low erosion risk areas, while agroforestry and pasture are usually associated with medium to high risk areas. This research implies that remote sensing and GIS provide promising tools for evaluating and mapping soil erosion risk in Amazonia.
650 $aRemote sensing
653 $aBrazilian Amazonia
653 $aGIS
653 $aRUSLE
653 $aSoil erosion risk
700 1 $aLI, G.
700 1 $aVALLADARES, G. S.
700 1 $aBATISTELLA, M.
773 $tLand Degradation & Development$gv. 15, p. 499-512, 2004.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Territorial (CNPM) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
URL |
Voltar
|
|
|
 | Acesso ao texto completo restrito à biblioteca da Embrapa Gado de Leite. Para informações adicionais entre em contato com cnpgl.biblioteca@embrapa.br. |
Registro Completo
|
Biblioteca(s): |
Embrapa Caprinos e Ovinos; Embrapa Gado de Leite. |
|
Data corrente: |
13/08/2021 |
|
Data da última atualização: |
14/08/2021 |
|
Tipo da produção científica: |
Artigo em Periódico Indexado |
|
Circulação/Nível: |
B - 1 |
|
Autoria: |
SILVA, R. P. A. da; LOBO, R. N. B.; EL FARO, L.; SANTOS, G. G. dos; BRUNELI, F. A. T.; PEIXOTO, M. G. C. D. |
|
Afiliação: |
ROBERTA POLYANA ARAÚJO DA SILVA, Universidade Federal do Ceará; RAIMUNDO NONATO BRAGA LOBO, CNPC; LENIRA EL FARO, Instituto de Zootecnia, Sertãozinho, SP; GLAUCYANA GOUVÊA DOS SANTOS; FRANK ANGELO TOMITA BRUNELI, CNPGL; MARIA GABRIELA CAMPOLINA D PEIXOTO, CNPGL. |
|
Título: |
Genetic parameters for somatic cell count (SCC) and milk production traits of Guzerá cows using data normalized by different procedures. |
|
Ano de publicação: |
2020 |
|
Fonte/Imprenta: |
Tropical Animal Health and Production, v. 52, p. 2513-2522, 2020. |
|
DOI: |
https://doi.org/10.1007/s11250-020-02277-8 |
|
Idioma: |
Inglês |
|
Conteúdo: |
This study aimed to estimate the genetic parameters for somatic cell count (SCC) and the genetic association between SCC and milk production traits using two different methods of SCC normalization. The dataset contained information on 8870 lactation records of 6172 Guzerá dairy cows selected for dual-purpose from 95 herds. The lactation means of SCC were normalized in two ways: (a) SCC1 = log10 (SCC) and (b) SCC2 = log2 (SCC/100) + 3. Multivariate analyses were performed considering milk production traits over the course of 305 days of lactation. Estimates of the variance components and genetic parameters were carried out by the Bayesian inference method, applying Gibbs sampling. Single chains of 2,000,000 iterations were used, with sampling discards of the first 5000 chains and a sampling period of every 50 iterations. The deviation of information criteria (DIC) was used to evaluate the best transformation for standardization of the SCC data, comparing analysis 1 (milk production traits over 305 days and SCC1) with analysis 2 (milk production traits over 305 days and SCC2). According to the data structure of this study, SCC1 normalization was the most efficient method and produced better estimates than normalization by the SCC2 method. The heritability estimates for SCC were low regardless of the transformation method used, indicating a small possibility of expressive genetic gains from the direct selection of these traits. However, the repeatability indicated the potential for increasing heritability estimates if the effects of the permanent environment were reduced. The genetic correlations between the milk yield and SCC traits do not indicate the possibility of a correlated genetic gain from the direct selection of one trait. However, concomitant selection for milk production traits and SCC will likely not affect the individual response either. MenosThis study aimed to estimate the genetic parameters for somatic cell count (SCC) and the genetic association between SCC and milk production traits using two different methods of SCC normalization. The dataset contained information on 8870 lactation records of 6172 Guzerá dairy cows selected for dual-purpose from 95 herds. The lactation means of SCC were normalized in two ways: (a) SCC1 = log10 (SCC) and (b) SCC2 = log2 (SCC/100) + 3. Multivariate analyses were performed considering milk production traits over the course of 305 days of lactation. Estimates of the variance components and genetic parameters were carried out by the Bayesian inference method, applying Gibbs sampling. Single chains of 2,000,000 iterations were used, with sampling discards of the first 5000 chains and a sampling period of every 50 iterations. The deviation of information criteria (DIC) was used to evaluate the best transformation for standardization of the SCC data, comparing analysis 1 (milk production traits over 305 days and SCC1) with analysis 2 (milk production traits over 305 days and SCC2). According to the data structure of this study, SCC1 normalization was the most efficient method and produced better estimates than normalization by the SCC2 method. The heritability estimates for SCC were low regardless of the transformation method used, indicating a small possibility of expressive genetic gains from the direct selection of these traits. However, the repeatability indicated the potential... Mostrar Tudo |
|
Palavras-Chave: |
Bayesian inference; Correlação genética; Herdabilidade; Inferência bayesiana; Mastite; Repetibilidade; Zebu cattle. |
|
Thesagro: |
Bovino; Gado Guzerá; Gado Zebu; Parâmetro Genético; Produção Leiteira. |
|
Thesaurus NAL: |
Genetic correlation; Heritability; Mastitis; Repeatability. |
|
Categoria do assunto: |
L Ciência Animal e Produtos de Origem Animal |
|
Marc: |
LEADER 03059naa a2200385 a 4500
001 2133570
005 2021-08-14
008 2020 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1007/s11250-020-02277-8$2DOI
100 1 $aSILVA, R. P. A. da
245 $aGenetic parameters for somatic cell count (SCC) and milk production traits of Guzerá cows using data normalized by different procedures.$h[electronic resource]
260 $c2020
520 $aThis study aimed to estimate the genetic parameters for somatic cell count (SCC) and the genetic association between SCC and milk production traits using two different methods of SCC normalization. The dataset contained information on 8870 lactation records of 6172 Guzerá dairy cows selected for dual-purpose from 95 herds. The lactation means of SCC were normalized in two ways: (a) SCC1 = log10 (SCC) and (b) SCC2 = log2 (SCC/100) + 3. Multivariate analyses were performed considering milk production traits over the course of 305 days of lactation. Estimates of the variance components and genetic parameters were carried out by the Bayesian inference method, applying Gibbs sampling. Single chains of 2,000,000 iterations were used, with sampling discards of the first 5000 chains and a sampling period of every 50 iterations. The deviation of information criteria (DIC) was used to evaluate the best transformation for standardization of the SCC data, comparing analysis 1 (milk production traits over 305 days and SCC1) with analysis 2 (milk production traits over 305 days and SCC2). According to the data structure of this study, SCC1 normalization was the most efficient method and produced better estimates than normalization by the SCC2 method. The heritability estimates for SCC were low regardless of the transformation method used, indicating a small possibility of expressive genetic gains from the direct selection of these traits. However, the repeatability indicated the potential for increasing heritability estimates if the effects of the permanent environment were reduced. The genetic correlations between the milk yield and SCC traits do not indicate the possibility of a correlated genetic gain from the direct selection of one trait. However, concomitant selection for milk production traits and SCC will likely not affect the individual response either.
650 $aGenetic correlation
650 $aHeritability
650 $aMastitis
650 $aRepeatability
650 $aBovino
650 $aGado Guzerá
650 $aGado Zebu
650 $aParâmetro Genético
650 $aProdução Leiteira
653 $aBayesian inference
653 $aCorrelação genética
653 $aHerdabilidade
653 $aInferência bayesiana
653 $aMastite
653 $aRepetibilidade
653 $aZebu cattle
700 1 $aLOBO, R. N. B.
700 1 $aEL FARO, L.
700 1 $aSANTOS, G. G. dos
700 1 $aBRUNELI, F. A. T.
700 1 $aPEIXOTO, M. G. C. D.
773 $tTropical Animal Health and Production$gv. 52, p. 2513-2522, 2020.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Caprinos e Ovinos (CNPC) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
Fechar
|
| Nenhum registro encontrado para a expressão de busca informada. |
|
|
