Meta-analysis of the relationship between crop yield and soybean rust severity

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) (70%, moderate=>40 and ≤70%, and low=≤40% S the check treatment), and growing season. The overall mean for r- (back-transformed Z-r) was -0.61, based on the random-effects model. DOT and DP explained 14 and 25%, respectively, of the variability in Z-r. Stronger associations (r-=-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 (DOT0.73 pp/%(-1)) were estimated for studies with DOT70%; 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.