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Biblioteca(s): |
Embrapa Meio Ambiente. |
Data corrente: |
05/03/2015 |
Data da última atualização: |
01/03/2023 |
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.; DEL PONTE, E. M. |
Afiliação: |
FELIPE DALLA LANA, UFRGS; PATRICIA K ZIEGELMANN, UFRGS; ALINE DE HOLANDA NUNES MAIA, CNPMA; CLAUDIA VIEIRA GODOY, CNPSO; EMERSON M DEL PONTE, UFRGS. |
Título: |
Meta-analysis of the relationship between crop yield and soybean rust severity. |
Ano de publicação: |
2015 |
Fonte/Imprenta: |
Phytopathology, St Paul, v. 105, n. 3, p. 307-315, 2015. |
Idioma: |
Inglês |
Conteúdo: |
Abstract: 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 analysis (k = 210) for the Y-S relationship were conducted and three effect-sizes were obtained from these analyses: 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-effects 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 (SEAS). The overall mean for r-hat (back-transformed Zr) was -0.61 based on the random-effects model. DOT and DP explained 14% and 25% of the variability in Zr. More strong associations (r-hat = -0.87 and -0.90) were estimated via mixed-effects models for the Zr data from studies with highest disease pressure (DP>70%) and earliest rust onset (DOT < R1). Overall means (based on a random-effect model) for the regression coefficients, ?0-hat and ?1-hat, were 2,977 kg/ha and 18 kg/ha/pp-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 ?1-hat but not in ?0-hat. The estimated relative reduction in Y ranged from 0.41 to 0.79 pp/pp-1 across seasons. Highest relative yield reductions (> 0.73 pp/pp-1) were estimated for studies where disease onset was prior to flowering (0.74 pp/pp-1) and lowest disease pressure (0.74 pp/pp-1; the latter possibly due to high fungicide efficacy when disease pressure is low, thus leading to higher yield differences between fungicide-protected and non-treated 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 be useful for crop loss assessments and economic analysis under scenarios of varying disease onset time and weather favorableness for epidemic development. MenosAbstract: 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 analysis (k = 210) for the Y-S relationship were conducted and three effect-sizes were obtained from these analyses: 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-effects 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 (SEAS). The overall mean for r-hat (back-transformed Zr) was -0.61 based on the random-effects model. DOT and DP explained 14% and 25% of the variability in Zr. More strong associations (r-hat = -0.87 and -0.90) were estimated via mixed-effects models for the Zr data from studies with highest disease pressure (DP>70%) and earliest rust onset (DOT < R1). Overall means (based on a random-effect model) for the regression coefficients, ?0-hat and ?1-hat, were 2,977 kg/ha and 18 kg/ha/pp-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 percent... Mostrar Tudo |
Thesagro: |
Análise estatística; Doença de planta; Ferrugem; Modelo matemático; Produtividade; Soja. |
Thesaurus NAL: |
Crop yield; ecology; epidemiology; Soybean rust; Statistical models. |
Categoria do assunto: |
H Saúde e Patologia |
Marc: |
LEADER 03238naa a2200301 a 4500 001 2010761 005 2023-03-01 008 2015 bl uuuu u00u1 u #d 100 1 $aDALLA LANA, F. 245 $aMeta-analysis of the relationship between crop yield and soybean rust severity.$h[electronic resource] 260 $c2015 520 $aAbstract: 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 analysis (k = 210) for the Y-S relationship were conducted and three effect-sizes were obtained from these analyses: 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-effects 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 (SEAS). The overall mean for r-hat (back-transformed Zr) was -0.61 based on the random-effects model. DOT and DP explained 14% and 25% of the variability in Zr. More strong associations (r-hat = -0.87 and -0.90) were estimated via mixed-effects models for the Zr data from studies with highest disease pressure (DP>70%) and earliest rust onset (DOT < R1). Overall means (based on a random-effect model) for the regression coefficients, ?0-hat and ?1-hat, were 2,977 kg/ha and 18 kg/ha/pp-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 ?1-hat but not in ?0-hat. The estimated relative reduction in Y ranged from 0.41 to 0.79 pp/pp-1 across seasons. Highest relative yield reductions (> 0.73 pp/pp-1) were estimated for studies where disease onset was prior to flowering (0.74 pp/pp-1) and lowest disease pressure (0.74 pp/pp-1; the latter possibly due to high fungicide efficacy when disease pressure is low, thus leading to higher yield differences between fungicide-protected and non-treated 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 be useful for crop loss assessments and economic analysis under scenarios of varying disease onset time and weather favorableness for epidemic development. 650 $aCrop yield 650 $aecology 650 $aepidemiology 650 $aSoybean rust 650 $aStatistical models 650 $aAnálise estatística 650 $aDoença de planta 650 $aFerrugem 650 $aModelo matemático 650 $aProdutividade 650 $aSoja 700 1 $aZIEGELMANN, P. K. 700 1 $aMAIA, A. de H. N. 700 1 $aGODOY, C. V. 700 1 $aDEL PONTE, E. M. 773 $tPhytopathology, St Paul$gv. 105, n. 3, p. 307-315, 2015.
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