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

Andrianasolo, Fety Nambinina and Casadebaig, Pierre and Maza, Elie and Champolivier, Luc and Maury, Pierre and Debaeke, Philippe Prediction of sunflower grain oil concentration as a function ofvariety, crop management and environment using statistical models. (2014) European Journal of Agronomy, 54. 84-96. ISSN 1161-0301

(Document in English)

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Official URL: http://dx.doi.org/10.1016/j.eja.2013.12.002


Sunflower (Helianthus annuus L.) raises as a competitive oilseed crop in the current environmentallyfriendly context. To help targeting adequate management strategies, we explored statistical models astools to understand and predict sunflower oil concentration. A trials database was built upon experi-ments carried out on a total of 61 varieties over the 2000–2011 period, grown in different locations inFrance 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 (multiplelinear regression, generalized additive model (GAM), regression tree (RT)) and compared to the refer-ence simple one of Pereyra-Irujo and Aguirrezábal (2007) based on 3 variables. Performance of modelswas 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 simplemodel led to poor results in our database (RMSEP = 3.33%; EF = 0.09). We computed hierarchical contribu-tion of predictors in each model by means of R2and concluded to the leading determination of potentialoil 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 mod-els and their domains of applicability are discussed. An improved statistical model (GAM-based) wasproposed for sunflower oil prediction on a large panel of genotypes grown in contrasting environments.

Item Type:Article
Additional Information:Thanks to Elsevier editor. The definitive version is available at http://www.sciencedirect.com/science/article/pii/S1161030113001834
HAL Id:hal-01186524
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
French research institutions > Institut National de la Recherche Agronomique - INRA (FRANCE)
Other partners > Centre Technique Interprofessionnel des Oléagineux Métropolitains - CETIOM (FRANCE)
Laboratory name:
Deposited On:25 Aug 2015 08:39

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