Dual ensemble approach to predict rice heading date by integrating multiple rice phenology models and machine learning-based genetic parameter regression models-Reference-Cited by-同舟云学术

Dual ensemble approach to predict rice heading date by integrating multiple rice phenology models and machine learning-based genetic parameter regression models

Published:2024-01 Issue: Volume:344 Page:109821
ISSN:0168-1923
Container-title:Agricultural and Forest Meteorology
language:en
Short-container-title:Agricultural and Forest Meteorology

Author:

Kawakita Satoshi,Yamasaki Masanori,Teratani Ryo,Yabe Shiori,Kajiya-Kanegae Hiromi,Yoshida Hiroe,Fushimi Erina,Nakagawa Hiroshi

Funder

Japan Science and Technology Agency

Core Research for Evolutional Science and Technology

Publisher

Elsevier BV

Subject

Atmospheric Science,Agronomy and Crop Science,Global and Planetary Change,Forestry

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5. Integrating genetic gain and gap analysis to predict improvements in crop productivity;Cooper;Crop Sci.,2020

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