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The development and validation of a yield loss prediction model for soybean rust

Presenter: J. Omielan

All authors and affiliations: Joe Omielan, Elena Prior, Jim Board, Cláudia Godoy, David Wright, Bob Kemerait, Weibo Dong, Abdullah Aqeel, and Saratha Kumudini

Disease control measures are economically viable when yield loss is sufficient to justify the cost of the control measures. Models that predict expected yield loss can serve as useful management decision aide tools. The objective of the current study was to develop and validate a simple yield loss prediction model for soybean rust, based on the mechanism by which the disease reduces soybean yield. In a two year study conducted in Brazil, SBR-induced yield loss was found to be due to: (i) accelerated leaf drop, (ii) reduced green leaf area (GLA) and (iii) reduced photosynthetic capacity of GLA. The reduced photosynthetic capacity of GLA has been quantified in controlled and field experiments. The three factors have been integrated into an effective leaf area duration (ELAD) value. A linear regression between ELAD and yield has been developed using data from Brazil (R² = 0.96) and is the basis of the yield loss model. Next, independent studies were conducted to validate the accuracy of model predictions over a range of environments, cultivar maturities and row widths, in the U.S. Trials were planted in Quincy, FL in 2007 and in Tifton, GA in 2008. In 2007 the trial included determinate and indeterminate MG 5 cultivars planted in 15 and 30 inch rows. In 2008 MG 7 and MG 8 cultivars were planted in 36 inch rows. Phenology, leaf area, disease severity and yield were measured. None of the trials had severe SBR epidemics resulting in rather limited yield losses. The addition of independent data sets from Brazil extended the range of yield losses. This allowed a better test of the fit of our model (R² = 0.66 and root mean square error (rmse) = 10.4). This affirms the ELAD/yield relationship across a number of regions and production practices. These data support our efforts to use our understanding of the mechanism of SBR yield loss to develop a reliable yield loss prediction tool.

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