Prediction of sunflower grain oil concentration as a function of variety, crop management and environment using statistical models

•We predicted sunflower oil concentration with 4 statistical models.•A wide range of varieties, soil water and nitrogen conditions constituted dataset.•25 physiologically-based variables were used as predictors.•GAM-based model performed best (R2=0.70; RMSEP=1.9).•Oil concentration was mainly linked...

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Bibliographic Details
Published in:European journal of agronomy 2014-03, Vol.54, p.84-96
Main Authors: Andrianasolo, Fety Nambinina, Casadebaig, Pierre, Maza, Elie, Champolivier, Luc, Maury, Pierre, Debaeke, Philippe
Format: Article
Language:English
Subjects:
Agronomy. Soil science and plant productions
Biological and medical sciences
Fundamental and applied biological sciences. Psychology
GAM
Genetics and breeding of economic plants
Genotype by environment interaction
Helianthus
Helianthus annuus
Regression model
Sunflower oil concentration
Varietal selection. Specialized plant breeding, plant breeding aims
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Summary:•We predicted sunflower oil concentration with 4 statistical models.•A wide range of varieties, soil water and nitrogen conditions constituted dataset.•25 physiologically-based variables were used as predictors.•GAM-based model performed best (R2=0.70; RMSEP=1.9).•Oil concentration was mainly linked to genotype rather than environmental factors. Sunflower (Helianthus annuus L.) raises as a competitive oilseed crop in the current environmentally friendly context. To help targeting adequate management strategies, we explored statistical models as tools to understand and predict sunflower oil concentration. A trials database was built upon experiments carried out on a total of 61 varieties over the 2000–2011 period, grown in different locations in France under contrasting management conditions (nitrogen fertilization, water regime, plant density). 25 literature-based predictors of seed oil concentration were used to build 3 statistical models (multiple linear regression, generalized additive model (GAM), regression tree (RT)) and compared to the reference simple one of Pereyra-Irujo and Aguirrezábal (2007) based on 3 variables. Performance of models was assessed by means of statistical indicators, including root mean squared error of prediction (RMSEP) and model efficiency (EF). GAM-based model performed best (RMSEP=1.95%; EF=0.71) while the simple model led to poor results in our database (RMSEP=3.33%; EF=0.09). We computed hierarchical contribution of predictors in each model by means of R2 and concluded to the leading determination of potential oil concentration (OC), followed by post-flowering canopy functioning indicators (LAD2 and MRUE2), plant nitrogen and water status and high temperatures effect. Diagnosis of error in the 4 statistical models and their domains of applicability are discussed. An improved statistical model (GAM-based) was proposed for sunflower oil prediction on a large panel of genotypes grown in contrasting environments.
ISSN:1161-0301
1873-7331
DOI:10.1016/j.eja.2013.12.002