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2. | | SILVA, M. V. G. B.; MARTINS, M. F.; CEMBRANELLI, M. DE A. R.; PANETTO, J. C. do C.; FREITAS, A. F. de; PAIVA, L. DE C.; GONÇALVES, G. S.; ALVES, B. R. C. Programa de mejoramiento genetico de la raza girolando - Evaluación Genética de Vacas - Junio 2016. Juiz de Fora: Embrapa Gado de Leite, 2016 40 p. (Embrapa Gado de Leite. Documentos, 194) Biblioteca(s): Embrapa Gado de Leite. |
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Registro Completo
Biblioteca(s): |
Embrapa Soja. |
Data corrente: |
21/07/2015 |
Data da última atualização: |
04/08/2017 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 1 |
Autoria: |
DALLA LANA, F.; ZIEGELMANN, P. K.; MAIA, A. de H. N.; GODOY, C. V.; PONTE, E. M. D. |
Afiliação: |
FELIPE DALLA LANA, UFRGS; PATRICIA K. ZIEGELMANN, UFRGS; ALINE DE HOLANDA NUNES MAIA, CNPMA; CLAUDIA VIEIRA GODOY, CNPSO; EMERSON M. DEL PONTE, UFV. |
Título: |
Meta-analysis of the relationship between crop yield And soybean rust severity. |
Ano de publicação: |
2015 |
Fonte/Imprenta: |
Phytopathology, v. 105, n. 3, p. 307-315, 2015. |
ISSN: |
0031-949X |
DOI: |
10.1094/PHYTO-06-14-0157-R |
Idioma: |
Português |
Conteúdo: |
Meta-analytic models were used to summarize and assess the heterogeneity in the relationship between soybean yield (Y, kg/ha) and rust severity (S, %) data from uniform fungicide trials (study, k) conducted over nine growing seasons in Brazil. For each selected study, correlation (k = 231) and regression (k = 210) analysis for the Y?S relationship were conducted and three effect-sizes were obtained from these analysis: Fisher?s transformation of the Pearson?s correlation coefficient (Zr) and the intercept ( 0) and slope ( 1) coefficients. These effect-sizes were summarized through random-effect and mixed-effect models, with the latter incorporating study-specific categorical moderators such as disease onset time (DOT) (70%, moderate = >40 and ≤70%, and low = ≤40% S the check treatment), and growing season. The overall mean for ̅ (backtransformed ̅ r) was ?0.61, based on the random-effects model. DOT and DP explained 14 and 25%, respectively, of the variability in ̅ r. Stronger associations (̅ = ?0.87 and ?0.90) were estimated by mixed-effects models for the Zr data from studies with highest DP (DP > 70%) and earliest rust onset (DOT < R1), respectively. Overall means (based on a random-effect model) for the regression coefficients 1051444; and were 2,977 and 18 kg/ha/%?1, respectively. In other words, S as low as 3% would reduce 60 kg/ha for an expected Y of 3,000 kg/ha. In relative terms, each unitary percent increase in S would lead to a 0.6 percentage point (pp) reduction in Y. The three categorical moderator variables explained some (5 to 10%) of the heterogeneity in but not in 1051444; . The estimated relative reduction in Y was 0.41 to 0.79 pp/%?1 across seasons. Highest relative yield reductions (>0.73 pp/%?1) were estimated for studies with DOT < R1 and DP > 70%; the latter possibly due to high fungicide efficacy when DP is low, thus leading to higher yield differences between fungicide-protected and nontreated plots. The critical-point meta-analytic models can provide general estimates of yield loss based on a composite measure of disease severity. They can also beuseful for crop loss assessments and economic analysis under scenarios of varying DOT and weather favorableness for epidemic development. MenosMeta-analytic models were used to summarize and assess the heterogeneity in the relationship between soybean yield (Y, kg/ha) and rust severity (S, %) data from uniform fungicide trials (study, k) conducted over nine growing seasons in Brazil. For each selected study, correlation (k = 231) and regression (k = 210) analysis for the Y?S relationship were conducted and three effect-sizes were obtained from these analysis: Fisher?s transformation of the Pearson?s correlation coefficient (Zr) and the intercept ( 0) and slope ( 1) coefficients. These effect-sizes were summarized through random-effect and mixed-effect models, with the latter incorporating study-specific categorical moderators such as disease onset time (DOT) (70%, moderate = >40 and ≤70%, and low = ≤40% S the check treatment), and growing season. The overall mean for ̅ (backtransformed ̅ r) was ?0.61, based on the random-effects model. DOT and DP explained 14 and 25%, respectively, of the variability in ̅ r. Stronger associations (̅ = ?0.87 and ?0.90) were estimated by mixed-effects models for the Zr data from studies with highest DP (DP > 70%) and earliest rust onset (DOT < R1), respectively. Overall means (based on a random-effect model) for the regression coefficients 1051444; ... Mostrar Tudo |
Palavras-Chave: |
Soybean. |
Categoria do assunto: |
-- |
Marc: |
LEADER 03223naa a2200205 a 4500 001 2020133 005 2017-08-04 008 2015 bl uuuu u00u1 u #d 022 $a0031-949X 024 7 $a10.1094/PHYTO-06-14-0157-R$2DOI 100 1 $aDALLA LANA, F. 245 $aMeta-analysis of the relationship between crop yield And soybean rust severity.$h[electronic resource] 260 $c2015 520 $aMeta-analytic models were used to summarize and assess the heterogeneity in the relationship between soybean yield (Y, kg/ha) and rust severity (S, %) data from uniform fungicide trials (study, k) conducted over nine growing seasons in Brazil. For each selected study, correlation (k = 231) and regression (k = 210) analysis for the Y?S relationship were conducted and three effect-sizes were obtained from these analysis: Fisher?s transformation of the Pearson?s correlation coefficient (Zr) and the intercept ( 0) and slope ( 1) coefficients. These effect-sizes were summarized through random-effect and mixed-effect models, with the latter incorporating study-specific categorical moderators such as disease onset time (DOT) (<R1 or ≥R1 reproductive crop stage), disease pressure (DP) (high = >70%, moderate = >40 and ≤70%, and low = ≤40% S the check treatment), and growing season. The overall mean for ̅ (backtransformed ̅ r) was ?0.61, based on the random-effects model. DOT and DP explained 14 and 25%, respectively, of the variability in ̅ r. Stronger associations (̅ = ?0.87 and ?0.90) were estimated by mixed-effects models for the Zr data from studies with highest DP (DP > 70%) and earliest rust onset (DOT < R1), respectively. Overall means (based on a random-effect model) for the regression coefficients and were 2,977 and 18 kg/ha/%?1, respectively. In other words, S as low as 3% would reduce 60 kg/ha for an expected Y of 3,000 kg/ha. In relative terms, each unitary percent increase in S would lead to a 0.6 percentage point (pp) reduction in Y. The three categorical moderator variables explained some (5 to 10%) of the heterogeneity in but not in . The estimated relative reduction in Y was 0.41 to 0.79 pp/%?1 across seasons. Highest relative yield reductions (>0.73 pp/%?1) were estimated for studies with DOT < R1 and DP > 70%; the latter possibly due to high fungicide efficacy when DP is low, thus leading to higher yield differences between fungicide-protected and nontreated plots. The critical-point meta-analytic models can provide general estimates of yield loss based on a composite measure of disease severity. They can also beuseful for crop loss assessments and economic analysis under scenarios of varying DOT and weather favorableness for epidemic development. 653 $aSoybean 700 1 $aZIEGELMANN, P. K. 700 1 $aMAIA, A. de H. N. 700 1 $aGODOY, C. V. 700 1 $aPONTE, E. M. D. 773 $tPhytopathology$gv. 105, n. 3, p. 307-315, 2015.
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