Sugarcane genomics & breeding
Saccharum officinarum · Brazil, India, Thailand, China, Pakistan
Sugarcane is a polyploid breeding nightmare and a sucrose engineering marvel. Our population-structure and genomic-selection modules cut through the genetic complexity to surface real predictive accuracy.
~1.9 billion tonnes of cane, the source of ~80% of global sugar.
Typical breeding goals
- •Cane yield and sucrose content
- •Smut and red rot resistance
- •Ratooning ability
- •Drought tolerance
Common challenges
- •Smut
- •Red rot
- •Borer
- •Drought
Pre-loaded trait library
When you upload sugarcane data, our phenotype column picker pre-suggests these standard traits so you don't start from a blank slate.
What you can run on sugarcane data
Every module below works on your uploaded sugarcane dataset. The math is crop-agnostic; the defaults are crop-aware.
Find SNPs significantly associated with any trait you've measured.
Predict GEBVs and cross-validate accuracy before deploying in the program.
PCA and ancestry decomposition to control for stratification.
GxE heatmap, AMMI, and Finlay–Wilkinson stability for cross-location data.
Rank parents by weighted multi-trait scores.
Map top hits to genes via Ensembl Plants.
Historical weather, GDD, heat-stress days.
Whole-genome GBLUP yield predictions on your dataset.
Start analyzing your sugarcane data
Upload a CSV, run a real GWAS or genomic-selection model, and get publication-ready output in minutes.
Get started