Prediction of municipality-level winter wheat yield based on meteorological data using machine learning in Hokkaido, Japan.

ABSTRACT: This study analyzed meteorological constraints on winter wheat yield in the northern Japanese island, Hokkaido, and developed a machine learning model to predict municipality-level yields from meteorological data. Compared to most wheat producing areas, this island is characterized by wet climate owing to greater annual precipitation and abundant snowmelt water supply in spring. Based on yield statistics collected from 119 municipalities for 14 years (N = 1,516) and high-resolution surface meteorological data, correlation analyses showed that precipitation, daily minimum air temperature, and irradiance during the grain-filling period had significant effects on the yield throughout the island while the effect of snow depth in early winter and spring was dependent on sites. Using 10-d mean m

SUBMITTER: Murakami K 

PROVIDER: S-EPMC8523044 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature