The Experts below are selected from a list of 327 Experts worldwide ranked by ideXlab platform
S Tartarini - One of the best experts on this subject based on the ideXlab platform.
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Genome wide association study of two phenology traits (flowering time and Maturity Date) in apple
Acta Horticulturae, 2017Co-Authors: Hélène Muranty, S Tartarini, Jorge Urrestarazu, Caroline Denancé, Diane Leforestier, Elisa Ravon, Arnaud Guyader, Rémi Guisnel, Laurence Feugey, L DondiniAbstract:The aim of Genome Wide Association Studies (GWAS) is to identify markers in tight linkage disequilibrium with loci controlling quantitative trait variation. These markers can then be used in marker-assisted selection (MAS) in fruit crops such as apple. The GWAS approach involves both phenotyping of a large population of mostly unrelated individuals for the traits of interest, and genotyping at high marker density. In the EU-FP7 project FruitBreedomics, almost 1,200 European diploid dessert apple accessions (old and/or local cultivars) from six germplasm collections were genotyped with the Affymetrix Axiom_Apple480K array (487,000 SNPs). Phenotypic data on a large number of traits have been gathered during the project. Here we focus on flowering period and harvesting Date. Knowledge of the genetic control of these traits is necessary to develop cultivars that can face the challenges imposed by global climate change and to target cultivar development as a function of a prolonged vegetation period in the production regions. Different models were tested, including control for effects of population structure and relatedness between cultivars. The full model, controlling for both structure and relatedness, was shown to be the most appropriate to avoid spurious marker-trait associations. When analyzing data over all collections, one significant marker-trait association was obtained for each trait, on chromosomes 9 and 3, for flowering period and harvesting Date, respectively. Thereby, genomic locations previously identified in bi-parental populations could now be confirmed for a genetically diverse germplasm.
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Fine mapping and identification of a candiDate gene for a major locus controlling Maturity Date in peach
BMC plant biology, 2013Co-Authors: Raul Pirona, L Dondini, S Tartarini, Iban Eduardo, Igor Pacheco, Cassia Da Silva Linge, Mara Miculan, Ignazio Verde, G. Pea, Daniele BassiAbstract:Background Maturity Date (MD) is a crucial factor for marketing of fresh fruit, especially those with limited shelf-life such as peach (Prunus persica L. Batsch): selection of several cultivars with differing MD would be advantageous to cover and extend the marketing season. Aims of this work were the fine mapping and identification of candiDate genes for the major Maturity Date locus previously identified on peach linkage group 4. To improve genetic resolution of the target locus two F2 populations derived from the crosses Contender x Ambra (CxA, 306 individuals) and PI91459 (NJ Weeping) x Bounty (WxBy, 103 individuals) were genotyped with the Sequenom and 9K Illumina Peach Chip SNP platforms, respectively.
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Comparison of the genetic determinism of two key phenological traits, flowering and Maturity Dates, in three Prunus species: peach, apricot and sweet cherry
Heredity, 2012Co-Authors: Elisabeth Dirlewanger, Loïck Le Dantec, J Quero-garcía, P Lambert, D Ruiz, L Dondini, E Illa, B Quilot-turion, J-m Audergon, S TartariniAbstract:The present study investigates the genetic determinism of flowering and Maturity Dates, two traits highly affected by global climate change. Flowering and Maturity Dates were evaluated on five progenies from three Prunus species, peach, apricot and sweet cherry, during 3–8 years. Quantitative trait locus (QTL) detection was performed separately for each year and also by integrating data from all years together. High heritability estimates were obtained for flowering and Maturity Dates. Several QTLs for flowering and Maturity Dates were highly stable, detected each year of evaluation, suggesting that they were not affected by climatic variations. For flowering Date, major QTLs were detected on linkage groups (LG) 4 for apricot and sweet cherry and on LG6 for peach. QTLs were identified on LG2, LG3, LG4 and LG7 for the three species. For Maturity Date, a major QTL was detected on LG4 in the three species. Using the peach genome sequence data, candiDate genes underlying the major QTLs on LG4 and LG6 were investigated and key genes were identified. Our results provide a basis for the identification of genes involved in flowering and Maturity Dates that could be used to develop cultivar ideotypes adapted to future climatic conditions.
Cheng-kun Kuo - One of the best experts on this subject based on the ideXlab platform.
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Pricing of Payment Deferred Vulnerable Options and Its Application to Vulnerable Range Accrual Notes
The International Journal of Business and Finance Research, 2012Co-Authors: Chih-wei Lee, Cheng-kun KuoAbstract:This paper derives a pricing model for payment deferred vulnerable options and applies the results to the pricing of vulnerable range accrual notes. The valuation model for vulnerable options takes into account the possibility of the option writer defaulting. However, when the payment Date is set later than the option Maturity Date, the valuation model will be incomplete if the default risk between the option Maturity and payment Dates is not explicitly incorporated. We extend the current available models and our results show that the default risk of the option writer will further reduce the option value if the payment Date is after the Maturity Date. The analysis of vulnerable range accrual note, which contains multiple payment deferred vulnerable options, is also performed. Due to the product design, the pricing model for vulnerable range accrual notes shows that the relationship between volatility and note value is not monotonic but depends on whether the underlying price is within, outside, or on the range boundary. JEL: G12; G13
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Pricing of Payment Deferred Vulnerable Options and its Application to Vulnerable Range Accrual Notes
2012Co-Authors: Arthur C. Lee, Cheng-kun KuoAbstract:This paper derives a pricing model for payment deferred vulnerable options and applies the results to the pricing of vulnerable range accrual notes. The valuation model for vulnerable options takes into account the possibility of the option writer defaulting. However, when the payment Date is set later than the option Maturity Date, the valuation model will be incomplete if the default risk between the option Maturity and payment Dates is not explicitly incorporated. We extend the current available models and our results show that the default risk of the option writer will further reduce the option value if the payment Date is after the Maturity Date. The analysis of vulnerable range accrual note, which contains multiple payment deferred vulnerable options, is also performed. Due to the product design, the pricing model for vulnerable range accrual notes shows that the relationship between volatility and note value is not monotonic but depends on whether the underlying price is within, outside, or on the range boundary.
L Dondini - One of the best experts on this subject based on the ideXlab platform.
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Genome wide association study of two phenology traits (flowering time and Maturity Date) in apple
Acta Horticulturae, 2017Co-Authors: Hélène Muranty, S Tartarini, Jorge Urrestarazu, Caroline Denancé, Diane Leforestier, Elisa Ravon, Arnaud Guyader, Rémi Guisnel, Laurence Feugey, L DondiniAbstract:The aim of Genome Wide Association Studies (GWAS) is to identify markers in tight linkage disequilibrium with loci controlling quantitative trait variation. These markers can then be used in marker-assisted selection (MAS) in fruit crops such as apple. The GWAS approach involves both phenotyping of a large population of mostly unrelated individuals for the traits of interest, and genotyping at high marker density. In the EU-FP7 project FruitBreedomics, almost 1,200 European diploid dessert apple accessions (old and/or local cultivars) from six germplasm collections were genotyped with the Affymetrix Axiom_Apple480K array (487,000 SNPs). Phenotypic data on a large number of traits have been gathered during the project. Here we focus on flowering period and harvesting Date. Knowledge of the genetic control of these traits is necessary to develop cultivars that can face the challenges imposed by global climate change and to target cultivar development as a function of a prolonged vegetation period in the production regions. Different models were tested, including control for effects of population structure and relatedness between cultivars. The full model, controlling for both structure and relatedness, was shown to be the most appropriate to avoid spurious marker-trait associations. When analyzing data over all collections, one significant marker-trait association was obtained for each trait, on chromosomes 9 and 3, for flowering period and harvesting Date, respectively. Thereby, genomic locations previously identified in bi-parental populations could now be confirmed for a genetically diverse germplasm.
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Fine mapping and identification of a candiDate gene for a major locus controlling Maturity Date in peach
BMC plant biology, 2013Co-Authors: Raul Pirona, L Dondini, S Tartarini, Iban Eduardo, Igor Pacheco, Cassia Da Silva Linge, Mara Miculan, Ignazio Verde, G. Pea, Daniele BassiAbstract:Background Maturity Date (MD) is a crucial factor for marketing of fresh fruit, especially those with limited shelf-life such as peach (Prunus persica L. Batsch): selection of several cultivars with differing MD would be advantageous to cover and extend the marketing season. Aims of this work were the fine mapping and identification of candiDate genes for the major Maturity Date locus previously identified on peach linkage group 4. To improve genetic resolution of the target locus two F2 populations derived from the crosses Contender x Ambra (CxA, 306 individuals) and PI91459 (NJ Weeping) x Bounty (WxBy, 103 individuals) were genotyped with the Sequenom and 9K Illumina Peach Chip SNP platforms, respectively.
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Comparison of the genetic determinism of two key phenological traits, flowering and Maturity Dates, in three Prunus species: peach, apricot and sweet cherry
Heredity, 2012Co-Authors: Elisabeth Dirlewanger, Loïck Le Dantec, J Quero-garcía, P Lambert, D Ruiz, L Dondini, E Illa, B Quilot-turion, J-m Audergon, S TartariniAbstract:The present study investigates the genetic determinism of flowering and Maturity Dates, two traits highly affected by global climate change. Flowering and Maturity Dates were evaluated on five progenies from three Prunus species, peach, apricot and sweet cherry, during 3–8 years. Quantitative trait locus (QTL) detection was performed separately for each year and also by integrating data from all years together. High heritability estimates were obtained for flowering and Maturity Dates. Several QTLs for flowering and Maturity Dates were highly stable, detected each year of evaluation, suggesting that they were not affected by climatic variations. For flowering Date, major QTLs were detected on linkage groups (LG) 4 for apricot and sweet cherry and on LG6 for peach. QTLs were identified on LG2, LG3, LG4 and LG7 for the three species. For Maturity Date, a major QTL was detected on LG4 in the three species. Using the peach genome sequence data, candiDate genes underlying the major QTLs on LG4 and LG6 were investigated and key genes were identified. Our results provide a basis for the identification of genes involved in flowering and Maturity Dates that could be used to develop cultivar ideotypes adapted to future climatic conditions.
Dongqin Yin - One of the best experts on this subject based on the ideXlab platform.
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Prediction of Winter Wheat Maturity Dates through Assimilating Remotely Sensed Leaf Area Index into Crop Growth Model
Remote Sensing, 2020Co-Authors: Wen Zhuo, Xinran Gao, Jianxi Huang, Jihua Meng, Hai Huang, Huailiang Chen, Dongqin YinAbstract:Predicting crop Maturity Dates is important for improving crop harvest planning and grain quality. The prediction of crop Maturity Dates by assimilating remote sensing information into crop growth model has not been fully explored. In this study, a data assimilation framework incorporating the leaf area index (LAI) product from Moderate Resolution Imaging Spectroradiometer (MODIS) into a World Food Studies (WOFOST) model was proposed to predict the Maturity Dates of winter wheat in Henan province, China. Minimization of normalized cost function was used to obtain the input parameters of the WOFOST model. The WOFOST model was run with the re-initialized parameter to forecast the Maturity Dates of winter wheat grid by grid, and THORPEX Interactive Grand Global Ensemble (TIGGE) was used as forecasting period weather input in the future 15 days (d) for the WOFOST model. The results demonstrated a promising regional Maturity Date prediction with determination coefficient (R2) of 0.94 and the root mean square error (RMSE) of 1.86 d. The outcomes also showed that the optimal forecasting starting time for Henan was 30 April, corresponding to a stage from anthesis to grain filling. Our study indicated great potential of using data assimilation approaches in winter wheat Maturity Date prediction.
Wen Zhuo - One of the best experts on this subject based on the ideXlab platform.
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Prediction of Winter Wheat Maturity Dates through Assimilating Remotely Sensed Leaf Area Index into Crop Growth Model
Remote Sensing, 2020Co-Authors: Wen Zhuo, Xinran Gao, Jianxi Huang, Jihua Meng, Hai Huang, Huailiang Chen, Dongqin YinAbstract:Predicting crop Maturity Dates is important for improving crop harvest planning and grain quality. The prediction of crop Maturity Dates by assimilating remote sensing information into crop growth model has not been fully explored. In this study, a data assimilation framework incorporating the leaf area index (LAI) product from Moderate Resolution Imaging Spectroradiometer (MODIS) into a World Food Studies (WOFOST) model was proposed to predict the Maturity Dates of winter wheat in Henan province, China. Minimization of normalized cost function was used to obtain the input parameters of the WOFOST model. The WOFOST model was run with the re-initialized parameter to forecast the Maturity Dates of winter wheat grid by grid, and THORPEX Interactive Grand Global Ensemble (TIGGE) was used as forecasting period weather input in the future 15 days (d) for the WOFOST model. The results demonstrated a promising regional Maturity Date prediction with determination coefficient (R2) of 0.94 and the root mean square error (RMSE) of 1.86 d. The outcomes also showed that the optimal forecasting starting time for Henan was 30 April, corresponding to a stage from anthesis to grain filling. Our study indicated great potential of using data assimilation approaches in winter wheat Maturity Date prediction.
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regional winter wheat Maturity Date prediction using remote sensing crop model data assimilation and numerical weather prediction
International Conference on Agro-Geoinformatics, 2018Co-Authors: Xinran Gao, Jianxi Huang, Wen Zhuo, Dehai ZhuAbstract:Optimizing harvesting schedules requires a method for Maturity Date prediction, to avoid the influence of adverse weather and prevent the decline of crop yield or quality due to inappropriate harvest schedule. However, most prediction models are statistical-based thus are not suitable for regional application, and remote sensing-based models lacked predictability. We presented a framework that assimilated leaf area index (LAI) derived from MODerate Resolution Imaging Spectroradiometer (MODIS) into WOrld FOod Studies (WOFOST) crop growth model, and forecast meteorological data from THORPEX Interactive Grand Global Ensemble (TIGGE) was used as weather data input for the future periods. We selected the winter wheat planting area in Henan Province as study area and recalibrated WOFOST model based on observation data from agrometeorological sites. A cost function based on normalization was constructed to quantify the difference between simulated LAI and MODIS LAI products. First the MODIS LAI profile was smoothed by Savitzky-Golay (S-G) filter, and then these two LAI profiles were normalized to keep their trend information. Then we selected parameters in WOFOST model that are sensitive to Maturity Date as optimization parameters, such as emergence Date (IDEM), effective temperature sum from emergence to anthesis (TSUM1) and effective temperature sum from anthesis to Maturity (TSUM2). These parameters have significant differences between years and no obvious spatial and temporal patterns. By means of Shuffled Complex Evolution method developed at the University of Arizona (SCE-UA) algorithm, we simulated in each pixel in the study area and retrieved the optimal parameters set of this pixel. Then we run WOFOST by this optimal parameter set to simulate the growth and development of winter wheat. Moreover, we transformed TIGGE data into the CABO-format weather file to drive WOFOST simulating winter wheat growth in the next 16 d and obtained a spatial distribution of winter wheat Maturity Date in the study area. Comparing the forecasting Date with the observed Date from agrometeorological sites, it demonstrated that this method had substantial accuracy in predicting regional Maturity Date with correlation coefficient (R2) of 0.90 and the root mean square error (RMSE) was 1.93 d. Besides that, the distribution map of Maturity prediction showed obvious spatial variability. This method can remedy the shortages of poor predictability and lacking regional differences in most previous methods, and it provides a reference for the future study of crop Maturity prediction at a regional scale with longer forecast period.
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Agro-Geoinformatics - Regional Winter Wheat Maturity Date Prediction Using Remote Sensing-Crop Model Data Assimilation and Numerical Weather Prediction
2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics), 2018Co-Authors: Xinran Gao, Jianxi Huang, Wen Zhuo, Dehai ZhuAbstract:Optimizing harvesting schedules requires a method for Maturity Date prediction, to avoid the influence of adverse weather and prevent the decline of crop yield or quality due to inappropriate harvest schedule. However, most prediction models are statistical-based thus are not suitable for regional application, and remote sensing-based models lacked predictability. We presented a framework that assimilated leaf area index (LAI) derived from MODerate Resolution Imaging Spectroradiometer (MODIS) into WOrld FOod Studies (WOFOST) crop growth model, and forecast meteorological data from THORPEX Interactive Grand Global Ensemble (TIGGE) was used as weather data input for the future periods. We selected the winter wheat planting area in Henan Province as study area and recalibrated WOFOST model based on observation data from agrometeorological sites. A cost function based on normalization was constructed to quantify the difference between simulated LAI and MODIS LAI products. First the MODIS LAI profile was smoothed by Savitzky-Golay (S-G) filter, and then these two LAI profiles were normalized to keep their trend information. Then we selected parameters in WOFOST model that are sensitive to Maturity Date as optimization parameters, such as emergence Date (IDEM), effective temperature sum from emergence to anthesis (TSUM1) and effective temperature sum from anthesis to Maturity (TSUM2). These parameters have significant differences between years and no obvious spatial and temporal patterns. By means of Shuffled Complex Evolution method developed at the University of Arizona (SCE-UA) algorithm, we simulated in each pixel in the study area and retrieved the optimal parameters set of this pixel. Then we run WOFOST by this optimal parameter set to simulate the growth and development of winter wheat. Moreover, we transformed TIGGE data into the CABO-format weather file to drive WOFOST simulating winter wheat growth in the next 16 d and obtained a spatial distribution of winter wheat Maturity Date in the study area. Comparing the forecasting Date with the observed Date from agrometeorological sites, it demonstrated that this method had substantial accuracy in predicting regional Maturity Date with correlation coefficient (R2) of 0.90 and the root mean square error (RMSE) was 1.93 d. Besides that, the distribution map of Maturity prediction showed obvious spatial variability. This method can remedy the shortages of poor predictability and lacking regional differences in most previous methods, and it provides a reference for the future study of crop Maturity prediction at a regional scale with longer forecast period.
