Technical Brief
Operational field targets, such as leaf layer numbers or leaf-area-to-fruit-weight ratios, are often used to guide cultural practices to achieve particular production objectives, e.g. reduced disease pressure, increased yield, and improved fruit composition. However, these targets are not customized to individual producers, nor do they account for the need to balance potentially conflicting objectives such as the desire to simultaneously increase fruit-zone sunlight exposure to reduce disease pressure, while also minimizing fruit exposure to avoid sunburn or the development of undesirable aroma compounds. The approach here balances multiple production objectives by adapting an algorithmic method for multi-objective optimization known as Non-dominated Sorting Genetic Algorithm. The method was demonstrated in a hypothetical situation where a grower has competing objectives of trying to reduce 1,1,6-trimethyl-1,2-dihydronaphthalene (TDN) precursors in Riesling while concurrently minimizing the amount of active ingredient used in their existing spray program. The demonstration model used two previously quantified responses for cluster exposure flux availability (CEFA) vs. TDN precursor concentrations, and one previously quantified response for CEFA vs. fruit spray residue. Optimal targets for CEFA and fruit spray inputs were computed that balance minimal TDN potential vs. minimal spray active ingredient. A second analysis demonstrated how evolving knowledge of field responses can be used to update vineyard targets to maintain an optimal balance among production objectives.
Sign in for ASEV members
ASEV Members, please sign in at ASEV to access the journal online.
Sign in for Institutional and Non-member Subscribers
Log in using your username and password
Pay Per Article - You may access this article (from the computer you are currently using) for 2 day for US$10.00
Regain Access - You can regain access to a recent Pay per Article purchase if your access period has not yet expired.