Incorporating machine learning with biophysical model can improve the evaluation of climate extremes impacts on wheat yield in south-eastern Australia-Reference-Cited by-同舟云学术

Incorporating machine learning with biophysical model can improve the evaluation of climate extremes impacts on wheat yield in south-eastern Australia

Published:2019-09 Issue: Volume:275 Page:100-113
ISSN:0168-1923
Container-title:Agricultural and Forest Meteorology
language:en
Short-container-title:Agricultural and Forest Meteorology

Author:

Feng Puyu,Wang Bin^ORCID,Liu De Li^ORCID,Waters Cathy,Yu Qiang^ORCID

Funder

China Scholarship Council (CSC)

Publisher

Elsevier BV

Subject

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

Reference92 articles.

1. World Agriculture Towards 2030/2050: the 2012 Revision;Alexandratos,2012

2. The impact of temperature variability on wheat yields;Asseng;Glob. Chang. Biol.,2011

3. Analysis of heat stress tolerance in winter wheat;Balla;Acta Agron. Hung.,2009

4. Simulating the impact of extreme heat and frost events on wheat crop production: a review;Barlow;Field Crops Res.,2015

5. Optimising grain yield and grazing potential of crops across Australia’s high rainfall zone: a simulation analysis. 1. Wheat;Bell;Crop Pasture Sci.,2014

Cited by 148 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Predicting rice phenology across China by integrating crop phenology model and machine learning;Science of The Total Environment;2024-11

2. Temporal and spatial patterns of extreme heat on wheat in China under climate change scenarios;Environmental and Experimental Botany;2024-10

3. Integrating machine learning with thermal-driven analytical energy balance model improved terrestrial evapotranspiration estimation through enhanced surface conductance;Remote Sensing of Environment;2024-09

4. Crop aboveground biomass monitoring model based on UAV spectral index reconstruction and Bayesian model averaging: A case study of film-mulched wheat and maize;Computers and Electronics in Agriculture;2024-09

5. A Stacking Ensemble Learning Model Combining a Crop Simulation Model with Machine Learning to Improve the Dry Matter Yield Estimation of Greenhouse Pakchoi;Agronomy;2024-08-14