Link to home

Meta-Analysis of the Relationship Between Crop Yield and Soybean Rust Severity

March 2015 , Volume 105 , Number  3
Pages  307 - 315

Felipe Dalla Lana, Patricia K. Ziegelmann, Aline de H. N. Maia, Cláudia V. Godoy, and Emerson M. Del Ponte

First and fifth authors: Departamento de Fitossanidade, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, 91540-000, Brazil; second author: Departamento de Estatística, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, 91509-900, Brazil; third author: Embrapa Meio Ambiente, Jaguariúna, SP, 13820-000, Brazil; and fourth author: Embrapa Soja, Londrina, PR, 86001-970, Brazil.

Go to article:
Accepted for publication 5 September 2014.

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 (β0) and slope (β1) 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   (back-transformed 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 ̅β̅0 and ̅β̅1 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 ̅β̅1 but not in ̅β̅0. 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 be useful for crop loss assessments and economic analysis under scenarios of varying DOT and weather favorableness for epidemic development.

© 2015 The American Phytopathological Society