Show simple item record

A data-driven simulation platform to predict cultivars’ performances under uncertain weather conditions

Author: De Los Campos, G.
Author: Perez-Rodriguez, P.
Author: Bogard, M.
Author: Gouache, D.
Author: Crossa, J.
Year: 2020
URI: https://hdl.handle.net/10883/20960
Format: PDF
Publisher: Nature Publishing Group
Relation with: https://hdl.handle.net/10883/21344
Copyright: CIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact CIMMYT-Knowledge-Center@cgiar.org indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose.
Type: Article
Place of Publication: London (United Kingdom)
Issue: 1
Volume: 11
DOI: 10.1038/s41467-020-18480-y
Description: In most crops, genetic and environmental factors interact in complex ways giving rise to substantial genotype-by-environment interactions (G×E). We propose that computer simulations leveraging field trial data, DNA sequences, and historical weather records can be used to tackle the longstanding problem of predicting cultivars? future performances under largely uncertain weather conditions. We present a computer simulation platform that uses Monte Carlo methods to integrate uncertainty about future weather conditions and model parameters. We use extensive experimental wheat yield data (n = 25,841) to learn G×E patterns and validate, using left-trial-out cross-validation, the predictive performance of the model. Subsequently, we use the fitted model to generate circa 143 million grain yield data points for 28 wheat genotypes in 16 locations in France, over 16 years of historical weather records. The phenotypes generated by the simulation platform have multiple downstream uses; we illustrate this by predicting the distribution of expected yield at 448 cultivar-location combinations and performing means-stability analyses.
Agrovoc: PLANT GENETICS
Agrovoc: DATA
Agrovoc: CROP PERFORMANCE
Agrovoc: SIMULATION MODELS
Related Datasets: https://www.nature.com/articles/s41467-020-18480-y#Sec18
ISSN: 2041-1723
Journal: Nature Communications
Article number: art. 4876


Files in this item

Thumbnail

This item appears in the following Collection(s)

  • Genetic Resources
    Genetic Resources including germplasm collections, wild relatives, genotyping, genomics, and IP

Show simple item record