Background

Nitrogen (N) is a crucial factor for wheat yield and baking quality. Due to environmental concerns, N inputs in Switzerland need to be reduced, which is further complicated by reduced water availability due to climate change. This poses a challenge as reducing N can affect baking quality.

Objectives

The project aims to breed wheat varieties that maintain high and stable baking quality under reduced N input. This requires an in-depth understanding of baking quality and advanced prediction methods in early breeding generations.

Approach

• Meta-Analysis of Historical Data: Compilation and analysis of existing data from two breeding programs and wheat processing.
• Analysis and Prediction at Genomic and Protein Levels: Characterisation of 200 lines at DNA and gluten levels to predict the indices derived in WP1.
• Prediction with Machine Learning Models: Use of various ML models to predict baking quality and derived indices from indirect traits, genotyping data, and gluten analyses.

This project will improve breeding strategies and lead to the development of wheat varieties that more efficiently convert available nitrogen into baking quality. In the long term, this contributes to more sustainable wheat production and secures Switzerland's self-sufficiency.