Integer programming as a powerful tool for polyclonal selection in ancient grapevine varieties.

Area of Science:

• Agricultural Science
• Genetics
• Optimization

Background:

• Polyclonal selection, crucial for ancient grapevine varieties, involves choosing a balanced mix of 7-20 clones.
• Current methods rely on empirical best linear unbiased predictors (BLUPs) from linear mixed models.
• The vine and wine sector requires selection methods that consider multiple target traits simultaneously.

Purpose of the Study:

• To introduce a novel multicriteria method for grapevine polyclonal selection.
• To implement integer programming for simultaneous consideration of multiple selection traits.
• To develop and test an algorithm for maximizing genetic gains in grapevine selection.

Main Methods:

• Application of integer programming as a novel approach for grapevine selection.
• Development of a multicriteria selection algorithm to maximize genetic gains.
• Testing the algorithm on real-world data from field trials of four ancient grapevine varieties.

Main Results:

• The integer programming method was successfully applied to polyclonal selection of grapevine clones.
• The algorithm effectively considered multiple traits simultaneously, maximizing genetic gains.
• Selected grapevine material exhibited high genetic gains in target traits without compromising other important characteristics.

Conclusions:

• Integer programming provides a powerful and effective tool for optimizing grapevine polyclonal selection.
• This method allows for the simultaneous management of multiple selection criteria, crucial for the vine and wine industry.
• The approach ensures the selection of superior grapevine material with desirable traits, supporting viticulture advancements.