A data-driven simulation platform to predict cultivars’ performances under uncertain weather conditions
| Autor: | José Crossa, David Gouache, Gustavo de los Campos, Paulino Pérez-Rodríguez, Matthieu Bogard |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0106 biological sciences
0301 basic medicine Agricultural genetics Computer science Yield (finance) Science Monte Carlo method General Physics and Astronomy Machine learning computer.software_genre 01 natural sciences General Biochemistry Genetics and Molecular Biology Field (computer science) Article Data-driven 03 medical and health sciences lcsh:Science Multidisciplinary business.industry Simulation modeling food and beverages General Chemistry 030104 developmental biology Data point Field trial Grain yield lcsh:Q Data integration Artificial intelligence business computer 010606 plant biology & botany |
| Zdroj: | Nature Communications Nature Communications, Vol 11, Iss 1, Pp 1-10 (2020) |
| ISSN: | 2041-1723 |
| Popis: | 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. Predicting crop performance in environments with limited field testing is challenging. Here the authors combine field experimental, DNA sequence, and weather data to predict genotypes’ future performance. They demonstrate the potential of this approach on a large dataset of wheat grain yield. |
| Databáze: | OpenAIRE |
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