Yield Loss Prediction Tool for Field-Specific Risk Management of Asian Soybean Rust

1. Yield Loss Prediction Tool for Field-Specific Risk Management of Asian Soybean Rust S. Kumudini, J. Omielan, C. Lee, J. Board, D. Hershman and C. Godoy

2. Southern Region at Particular Risk Fig. 1. Probability of at least 15 consecutive days of suitable conditions for P. pachyrhizi infection between July 1 and Sept 30. Hatched areas indicate major or minor soybean production areas. Source: R. Magarey, USDA/NCSU Image Source: http://www.farmassist.com/soybeanrust/Navigation.aspx?nav=probability.html

3. The Problem • Soybean rust may require one or more fungicide applications each season • $10-$35/acre per application • Not using a fungicide may result in severe yield loss • Use of multiple fungicide applications may result in a net economic loss

4. Soybean Rust Arrived Late in 2005 http://www.sbrusa.net/

5. Impact of Soybean Rust on yield is dependent on Growth Stage Fig. 2. Soybean canopy at various stages of reproductive development. Note the different levels of natural leaf defoliation that occurs as the crop reaches maturity.

6. Central Question? • If soybean rust comes late, what field and economic situations justify a fungicide application?

7. Objective To develop a yield loss prediction model for Soybean Rust damage: specific to maturity group, and growth stage.

8. Yield loss prediction model • Current thinking: soybean rust causes yield loss due to crop defoliation • Prototype model uses hail damage data: • Model yield loss based on % defoliation(see our prototype). • More accurate estimates of yield loss are based on leaf area remaining • % defoliation is less accurate (see Board et al., 1994, 1997; Browde et al., 1994; Board, 2004)

9. Methodology • Check assumption: Is yield loss due to defoliation alone(Londrina, Brazil)? • Model building: Determine relationship between remaining leaf area and yield potential(across maturity groups and at different growth stages - studies in Louisiana and Kentucky). • Software development: Develop software which will use producer-specified informationto calculate current potential yield and potential yield loss due to SBR.

10. Experiment Station: Embrapa soja, Londrina, Brazil

11. Progress in Brazil. I. Check assumption -Is yield loss due to defoliation alone Cultivar is BRS 154 (MG VII) Rows 45 cm (~18 inches) Planted 20 December, 2006

12. Progress in Brazil. I. Check assumption -Is yield loss due to defoliation alone Disease severity monitored during the growing season using the scale below

13. Progress in Brazil. I. Check assumption -Is yield loss due to defoliation alone A) B) Images taken 15 March 2006 by Dr. C. Godoy. Plots in frame A show see significant leaf loss due to ASR, B) Fungicide treated plots were manually defoliated weekly such that we see similar leaf area in plots that were naturally defoliated by ASR (image A) as well as those that were manually defoliated to simulate the defoliation impact of ASR (image B).

14. Progress in USA.II. Model building - Determine relationship between remaining leaf area and yield potential • Model development dependent on work in KY and LA • Field plans still in the process of modification at both Lexington, KY and Baton Rouge, LA • Potential to validate data in other southern states with incidences of SBR. Collaborator in Clemson University

15. Progress in USA (KY). III. Software development - Develop software which calculates current potential yield and potential yield loss due to SBR. • Website established. Explains project objectives and gives on-going project developments: • http://www.uky.edu/Ag/Agronomy/Department/sbr/index.htm