Analyzing moisture-heat coupling in a wheat-soil system using data-driven vector autoregression model.

ABSTRACT: Soil temperature and moisture have a close relationship, the accurate controlling of which is important for crop growth. Mechanistic models built by previous studies need exhaustive parameters and seldom consider time stochasticity and lagging effect. To circumvent these problems, this study designed a data-driven stochastic model analyzing soil moisture-heat coupling. Firstly, three vector autoregression models are built using hourly data on soil moisture and temperature at the depth of 10, 30, and 90 cm. Secondly, from impulse response functions, the time lag and intensity of two variables' response to one unit of positive shock can be obtained, which describe the time length and strength at which temperature and moisture affect each other, indicating the degree of coupling. Thirdly, Gra

SUBMITTER: Feng X 

PROVIDER: S-EPMC6568251 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature