Integrated phenology and climate in rice yields prediction using machine learning methods
Rice (Oryza sativa L.) is a staple cereal crop and its demand is substantially increasing with the growth of the global population. Precisely predicting rice yields are of vital importance to ensure the food security in countries like China, where rice accounts for one-fifth of the total agricultura...
Enregistré dans:
Auteurs principaux: | Yahui Guo, Yongshuo Fu, Fanghua Hao, Xuan Zhang, Wenxiang Wu, Xiuliang Jin, Christopher Robin Bryant, J. Senthilnath |
---|---|
Format: | article |
Langue: | EN |
Publié: |
Elsevier
2021
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/681319c8e6d3410cbf182d64fffda8c6 |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
Targeted yield precision model assessment for rice-rice crop sequences in Farmers' Fields in Humid, Sub-tropical Northeastern India
par: Das,K.N, et autres
Publié: (2016) -
Impact of Nursery Seeding Density, Nitrogen, and Seedling Age on Yield and Yield Attributes of Fine Rice
par: Sarwar,Naeem, et autres
Publié: (2011) -
Relationships between grain yield and agronomic traits of rice in southern China
par: Zhao,Hua, et autres
Publié: (2020) -
GGE biplot analysis of multi-environment yield trials of rice produced in a temperate climate
par: Donoso-Ñanculao,Gabriel, et autres
Publié: (2016) -
Effects of submergence stress at the vegetative growth stage on hybrid rice growth and grain yield in China
par: Chen,Yutiao, et autres
Publié: (2021)