Integration of genomics with crop modeling for predicting rice days to flowering: A multi-model analysis

Yang Yubin, Wilson Lloyd Ted, Li Tao, Paleari Livia, Confalonieri Roberto, Zhu Yan, Tang Liang, Qiu Xiaolei, Tao Fulu, Chen Yi, Hoogenboom Gerrit, Boote Ken, Gao Yujing, Onogi Akio, Nakagawa Hiroshi, Yoshida Hiroe, Yabe Shiori, Dingkuhn Michael, Lafarge Tanguy, Wang Jing, Hasegawa Toshihiro. 2021. Integration of genomics with crop modeling for predicting rice days to flowering: A multi-model analysis. Field Crops Research, 276:108394, 15 p.

Quartile : Outlier, Sujet : AGRONOMY

Résumé : The ability of crop models to decompose complex traits and integrate the underlying processes enables them to capture genotype-environment interactions in diverse environments. Integrating genomics with biophysical crop models represents a potential breakthrough technology for improving our understanding of genotype-environment interactions across the biological organization. We present the results of a multi-model analysis on integrating crop modeling with genomic prediction. Seven rice models were evaluated on their ability to predict days to flowering in ten environments from parameters estimated through genome-wide association and genomic prediction, using a 13-fold cross-validation scheme. Phenotypic data were based on a rice diversity panel of 169 accessions with 700k markers. Significant associations with known flowering genes were identified for several model parameters. Although high accuracy was achieved for genomic prediction of model parameters in calibration, prediction accuracy was low for untested genotypes. We observed divergent model performance using genomic-predicted model parameters, which was attributed to photoperiod and temperature response curves, and number of calibrated model parameters. Several areas were identified for further research that could lead to better understanding the genetic control of complex trait formation and improved integration of genomics with crop modeling.

Mots-clés Agrovoc : intéraction génotype environnement, modélisation des cultures, génome, Oryza sativa, modèle mathématique, floraison, technique de prévision, interactions biologiques, photopériodicité, génotype, modélisation, variation génétique, génie génétique, fleur, développement biologique

Mots-clés libres : Rice, Phenology, Modeling, Genomic prediction, Model comparison

Classification Agris : F62 - Physiologie végétale - Croissance et développement
U30 - Méthodes de recherche

Champ stratégique Cirad : CTS 2 (2019-) - Transitions agroécologiques

Agences de financement hors UE : Texas AgriLife Research, National Natural Science Foundation of China, Japan Science and Technology Agency

Auteurs et affiliations

• Yang Yubin, Texas A&M AgriLife Research (USA) - auteur correspondant
• Wilson Lloyd Ted, Texas A&M AgriLife Research (USA)
• Li Tao, DNDC Applications Research and Training (USA)
• Paleari Livia, University of Milan (ITA)
• Confalonieri Roberto, University of Milan (ITA)
• Zhu Yan, National Engineering and Technology Center for Information Agriculture (CHN)
• Tang Liang, National Engineering and Technology Center for Information Agriculture (CHN)
• Qiu Xiaolei, Nanjing Agricultural University (CHN)
• Tao Fulu, CAS (CHN)
• Chen Yi, CAS (CHN)
• Hoogenboom Gerrit, University of Florida (USA)
• Boote Ken, University of Florida (USA)
• Gao Yujing, University of Florida (USA)
• Onogi Akio, Ryukoku University (JPN)
• Nakagawa Hiroshi, NARO (JPN)
• Yoshida Hiroe, NARO (JPN)
• Yabe Shiori, NARO (JPN)
• Dingkuhn Michael, CIRAD-BIOS-UMR AGAP (FRA)
• Lafarge Tanguy, CIRAD-BIOS-UMR AGAP (FRA)
• Wang Jing, Texas A&M AgriLife Research (USA)
• Hasegawa Toshihiro, NARO (JPN)

Source : Cirad-Agritrop (https://agritrop.cirad.fr/600786/)

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