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


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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




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



Soybean Rust Arrived Late in 2005





Central Question?



Objective

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



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)


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 information to calculate current potential yield and potential yield loss due to SBR.



Experiment Station: Embrapa soja, Londrina, Brazil



Progress in Brazil. I. Check assumption - Is yield loss due to defoliation alone



Progress in Brazil. I. Check assumption - Is yield loss due to defoliation alone



Progress in Brazil. I. Check assumption - Is yield loss due to defoliation alone



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



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



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