The Experts below are selected from a list of 132339 Experts worldwide ranked by ideXlab platform
Philippe Debaeke - One of the best experts on this subject based on the ideXlab platform.
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Analysis and modelling of the factors controlling seed Oil Concentration in sunflower: a review
OCL Oilseeds and fats crops and lipids, 2016Co-Authors: Fety Andrianasolo, Luc Champolivier, Philippe Debaeke, Pierre MauryAbstract:Sunflower appears as a potentially highly competitive crop, thanks to the diversification of its market and the richness of its Oil. However, seed Oil Concentration (OC) - a commercial criterion for crushing industry - is subjected to genotypic and environmental effects that make it sometimes hardly predictable. It is assumed that more understanding of Oil physiology combined with the use of crop models should permit to improve prediction and management of grain quality for various end-users. Main effects of temperature, water, nitrogen, plant density and fungal diseases were reviewed in this paper. Current generic and specific crop models which simulate Oil Concentration were found to be empirical and to lack of proper evaluation processes. Recently two modeling approaches integrating ecophysiological knowledge were developed by Andrianasolo (2014, Statistical and dynamic modelling of sunflower (Helianthus annuus L.) grain composition as a function of agronomic and environmental factors, Ph.D. Thesis, INP Toulouse): (i) a statistical approach relating OC to a range of explanatory variables (potential OC, temperature, water and nitrogen stress indices, intercepted radiation, plant density) which resulted in prediction quality from 1.9 to 2.5 Oil points depending on the nature of the models; (ii) a dynamic approach, based on "source-sink" relationships involving leaves, stems, receptacles (as sources) and hulls, proteins and Oil (as sinks) and using priority rules for carbon and nitrogen allocation. The latter model reproduced dynamic patterns of all source and sink components faithfully, but tended to overestimate OC. A better description of photosynthesis and nitrogen uptake, as well as genotypic parameters is expected to improve its performance.
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Prediction of sunflower grain Oil Concentration as a function ofvariety, crop management and environment using statistical models
European Journal of Agronomy, 2015Co-Authors: Fety Nambinina Andrianasolo, Pierre Maury, Pierre Casadebaig, Elie Maza, Luc Champolivier, Philippe DebaekeAbstract: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.
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Prediction of sunflower grain Oil Concentration as a function of variety, crop management and environment using statistical models
European Journal of Agronomy, 2014Co-Authors: Fety Nambinina Andrianasolo, Pierre Maury, Pierre Casadebaig, Elie Maza, Luc Champolivier, Philippe DebaekeAbstract: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 Aguirrezabal (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.
Fety Nambinina Andrianasolo - One of the best experts on this subject based on the ideXlab platform.
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Prediction of sunflower grain Oil Concentration as a function ofvariety, crop management and environment using statistical models
European Journal of Agronomy, 2015Co-Authors: Fety Nambinina Andrianasolo, Pierre Maury, Pierre Casadebaig, Elie Maza, Luc Champolivier, Philippe DebaekeAbstract: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.
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Prediction of sunflower grain Oil Concentration as a function of variety, crop management and environment using statistical models
European Journal of Agronomy, 2014Co-Authors: Fety Nambinina Andrianasolo, Pierre Maury, Pierre Casadebaig, Elie Maza, Luc Champolivier, Philippe DebaekeAbstract: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 Aguirrezabal (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.
Luc Champolivier - One of the best experts on this subject based on the ideXlab platform.
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Analysis and modelling of the factors controlling seed Oil Concentration in sunflower: a review
OCL Oilseeds and fats crops and lipids, 2016Co-Authors: Fety Andrianasolo, Luc Champolivier, Philippe Debaeke, Pierre MauryAbstract:Sunflower appears as a potentially highly competitive crop, thanks to the diversification of its market and the richness of its Oil. However, seed Oil Concentration (OC) - a commercial criterion for crushing industry - is subjected to genotypic and environmental effects that make it sometimes hardly predictable. It is assumed that more understanding of Oil physiology combined with the use of crop models should permit to improve prediction and management of grain quality for various end-users. Main effects of temperature, water, nitrogen, plant density and fungal diseases were reviewed in this paper. Current generic and specific crop models which simulate Oil Concentration were found to be empirical and to lack of proper evaluation processes. Recently two modeling approaches integrating ecophysiological knowledge were developed by Andrianasolo (2014, Statistical and dynamic modelling of sunflower (Helianthus annuus L.) grain composition as a function of agronomic and environmental factors, Ph.D. Thesis, INP Toulouse): (i) a statistical approach relating OC to a range of explanatory variables (potential OC, temperature, water and nitrogen stress indices, intercepted radiation, plant density) which resulted in prediction quality from 1.9 to 2.5 Oil points depending on the nature of the models; (ii) a dynamic approach, based on "source-sink" relationships involving leaves, stems, receptacles (as sources) and hulls, proteins and Oil (as sinks) and using priority rules for carbon and nitrogen allocation. The latter model reproduced dynamic patterns of all source and sink components faithfully, but tended to overestimate OC. A better description of photosynthesis and nitrogen uptake, as well as genotypic parameters is expected to improve its performance.
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Prediction of sunflower grain Oil Concentration as a function ofvariety, crop management and environment using statistical models
European Journal of Agronomy, 2015Co-Authors: Fety Nambinina Andrianasolo, Pierre Maury, Pierre Casadebaig, Elie Maza, Luc Champolivier, Philippe DebaekeAbstract: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.
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Prediction of sunflower grain Oil Concentration as a function of variety, crop management and environment using statistical models
European Journal of Agronomy, 2014Co-Authors: Fety Nambinina Andrianasolo, Pierre Maury, Pierre Casadebaig, Elie Maza, Luc Champolivier, Philippe DebaekeAbstract: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 Aguirrezabal (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.
Pierre Maury - One of the best experts on this subject based on the ideXlab platform.
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Analysis and modelling of the factors controlling seed Oil Concentration in sunflower: a review
OCL Oilseeds and fats crops and lipids, 2016Co-Authors: Fety Andrianasolo, Luc Champolivier, Philippe Debaeke, Pierre MauryAbstract:Sunflower appears as a potentially highly competitive crop, thanks to the diversification of its market and the richness of its Oil. However, seed Oil Concentration (OC) - a commercial criterion for crushing industry - is subjected to genotypic and environmental effects that make it sometimes hardly predictable. It is assumed that more understanding of Oil physiology combined with the use of crop models should permit to improve prediction and management of grain quality for various end-users. Main effects of temperature, water, nitrogen, plant density and fungal diseases were reviewed in this paper. Current generic and specific crop models which simulate Oil Concentration were found to be empirical and to lack of proper evaluation processes. Recently two modeling approaches integrating ecophysiological knowledge were developed by Andrianasolo (2014, Statistical and dynamic modelling of sunflower (Helianthus annuus L.) grain composition as a function of agronomic and environmental factors, Ph.D. Thesis, INP Toulouse): (i) a statistical approach relating OC to a range of explanatory variables (potential OC, temperature, water and nitrogen stress indices, intercepted radiation, plant density) which resulted in prediction quality from 1.9 to 2.5 Oil points depending on the nature of the models; (ii) a dynamic approach, based on "source-sink" relationships involving leaves, stems, receptacles (as sources) and hulls, proteins and Oil (as sinks) and using priority rules for carbon and nitrogen allocation. The latter model reproduced dynamic patterns of all source and sink components faithfully, but tended to overestimate OC. A better description of photosynthesis and nitrogen uptake, as well as genotypic parameters is expected to improve its performance.
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Prediction of sunflower grain Oil Concentration as a function ofvariety, crop management and environment using statistical models
European Journal of Agronomy, 2015Co-Authors: Fety Nambinina Andrianasolo, Pierre Maury, Pierre Casadebaig, Elie Maza, Luc Champolivier, Philippe DebaekeAbstract: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.
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Prediction of sunflower grain Oil Concentration as a function of variety, crop management and environment using statistical models
European Journal of Agronomy, 2014Co-Authors: Fety Nambinina Andrianasolo, Pierre Maury, Pierre Casadebaig, Elie Maza, Luc Champolivier, Philippe DebaekeAbstract: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 Aguirrezabal (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.
Elie Maza - One of the best experts on this subject based on the ideXlab platform.
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Prediction of sunflower grain Oil Concentration as a function ofvariety, crop management and environment using statistical models
European Journal of Agronomy, 2015Co-Authors: Fety Nambinina Andrianasolo, Pierre Maury, Pierre Casadebaig, Elie Maza, Luc Champolivier, Philippe DebaekeAbstract: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.
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Prediction of sunflower grain Oil Concentration as a function of variety, crop management and environment using statistical models
European Journal of Agronomy, 2014Co-Authors: Fety Nambinina Andrianasolo, Pierre Maury, Pierre Casadebaig, Elie Maza, Luc Champolivier, Philippe DebaekeAbstract: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 Aguirrezabal (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.
