A model-based approach to assist variety evaluation in sunflower crop

•Assessing the characteristics of new varieties is a key component of crop improvement.•The variety testing process is exclusively based on experimental field approaches.•Crop modeling allows to amplify the environmental conditions in evaluation networks.•Predictions were accurate enough to discrimi...

Full description

Saved in:
Bibliographic Details
Published in:European journal of agronomy 2016-11, Vol.81, p.92-105
Main Authors: Casadebaig, Pierre, Mestries, Emmanuelle, Debaeke, Philippe
Format: Article
Language:English
Subjects:
Agricultural sciences
Botanics
Computer Science
Crop management
Crop model
Genotype by environment interactions
Life Sciences
Modeling and Simulation
Multi-environment trials
Vegetal Biology
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•Assessing the characteristics of new varieties is a key component of crop improvement.•The variety testing process is exclusively based on experimental field approaches.•Crop modeling allows to amplify the environmental conditions in evaluation networks.•Predictions were accurate enough to discriminate between class of variety performance.•Spatial management of genetic resources could reduce yield gap. Assessing the performance and the characteristics (e.g. yield, quality, disease resistance, abiotic stress tolerance) of new varieties is a key component of crop performance improvement. However, the variety testing process is presently exclusively based on experimental field approaches which inherently reduces the number and the diversity of experienced combinations of varieties×environmental conditions in regard of the multiplicity of growing conditions within the cultivation area. Our aim is to make a greater and faster use of the information issuing from these trials using crop modeling and simulation to amplify the environmental and agronomic conditions in which the new varieties are tested. In this study, we present a model-based approach to assist variety testing and implement this approach on sunflower crop, using the SUNFLO simulation model and a subset of 80 trials from a large multi-environment trial (MET) conducted each year by agricultural extension services to compare newly released sunflower hybrids. After estimating parameter values (using plant phenotyping) to account for new genetic material, we independently evaluated the model prediction capacity on the MET (relative RMSE for oil yield was 16.4%; model accuracy was 54.4%) and its capacity to rank commercial hybrids for performance level (relative RMSE was 11%; Kendall's τ=0.41, P
ISSN:1161-0301
1873-7331
DOI:10.1016/j.eja.2016.09.001