A Dual Ensemble Agroclimate Modelling Procedure to Assess Climate Change Impacts on Sugarcane Production in Australia

Journals Menu

Follow SCIRP

	customer@scirp.org
	+86 18163351462(WhatsApp)
	1655362766

	Paper Publishing WeChat

Agricultural Sciences

ISSN Print: 2156-8553
ISSN Online: 2156-8561
www.scirp.org/journal/as
E-mail: as@scirp.org

Google-based Impact Factor: 1.22
Citations

Journals Menu

"A Dual Ensemble Agroclimate Modelling Procedure to Assess Climate Change Impacts on Sugarcane Production in Australia"
written by Yvette Everingham, Geoff Inman-Bamber, Justin Sexton, Chris Stokes,
published by Agricultural Sciences, Vol.6 No.8, 2015

has been cited by the following article(s):

Google Scholar
CrossRef

[1]	Evaluating and improving crop growth models for simulating genotype-by-environment interactions in sugarcane
	2023

[2]	Denitrification losses (N2O & N2) in response to nitrogen fertiliser rates in Australian sugarcane systems: From field to regional scales
	2023

[3]	Climate change and Australia's primary industries: factors hampering an effective and coordinated response
	International Journal of …, 2022

[4]	Potential for sugarcane production under current and future climates in South Africa: sugar and ethanol yields, and crop water use
	Sugar Tech, 2022

[5]	Sugarcane Yield Prediction Using Vegetation Indices in Northern Karnataka, India
	2022

[6]	Employment Challenges in Agriculture in Australia
	Tamansiswa Accounting Jurnal International, 2021

[7]	The impact of climate change and climate extremes on sugarcane production
	2021

[8]	Employment Challenges in Agriculture in Australia: English
	Tamansiswa Accounting Journal International, 2021

[9]	A Random Forest Algorithm for Predicting Crop Yield in Hilly Regions of North East India
	2021

[10]	Global Optimization of Cultivar Trait Parameters in the Simulation of Sugarcane Phenology Using Gaussian Process Emulation
	2021

[11]	Sugarcane: Contribution of Process-Based Models for Understanding and Mitigating Impacts of Climate Variability and Change on Production
	2020

[12]	An improved workflow for calibration and downscaling of GCM climate forecasts for agricultural applications–A case study on prediction of sugarcane yield in Australia
	2020

[13]	Crop Yield Estimation Using Decision Trees and Random Forest Machine Learning Algorithms on Data from Terra (EOS AM-1) & Aqua (EOS PM-1) Satellite Data
	2020

[14]	Traits for canopy development and light interception by twenty-seven Brazilian sugarcane varieties
	2020

[15]	A Multi-layered Convolutional Neural Network for Soil Variables Estimation with the Combination of Open Access Data
	Conference of the …, 2020

[16]	Predicting the effect of climate change on sugarcane cultivation Fábio R. Marin, University of São Paulo (USP)-Luiz de Queiroz College of Agriculture …
	Achieving …, 2019

[17]	Modeling Environmental Actions of Corporate Sustainable Activity: Evidence from Lithuania
	2019

[18]	The impact of climate change on the Australian sugarcane industry
	2019

[19]	Crop Yield Estimation Using Decision Trees and Random Forest Machine Learning Algorithms on Data from Terra
	2019

[20]	Site-specific assessment of spatial and temporal variability of sugarcane yield related to soil attributes
	Geoderma, 2019

[21]	New APSIM-Sugar features and parameters required to account for high sugarcane yields in tropical environments
	2019

[22]	Spatial and temporal variability of soil attributes and their relationship with crop yield, topographic parameters and apparent electrical conductivity (ECa) in sugarcane …
	2018

[23]	Implications of climate change for the sugarcane industry
	Wiley Interdisciplinary Reviews: Climate Change, 2018

[24]	Refining the Canegro model for improved simulation of climate change impacts on sugarcane
	European Journal of Agronomy, 2018

[25]	Future climate change projects positive impacts on sugarcane productivity in southern China
	European Journal of Agronomy, 2018

[26]	Predicting the effect of climate change on sugarcane cultivation
	2018

[27]	Data Mining Efficiency in Auctions on the basis of Random Forest Algorithm A Case Study on Source-Area Wholesale Market in Atsumi Area, Aichi Prefecture
	The Agricultural Marketing Journal of Japan, 2018

[28]	Measuring and modelling CO 2 effects on sugarcane
	Environmental Modelling & Software, 2016

[29]	Accurate prediction of sugarcane yield using a random forest algorithm
	Agronomy for Sustainable Development, 2016

[30]	Quantification of the effects of climate warming and crop management on sugarcane phenology
	2016

[31]	Measuring and modelling CO2 effects on sugarcane
	Environmental Modelling & Software, 2016

[32]	Bayesian statistical calibration of variety parameters in a sugarcane crop model
	2015

[33]	Climate change will impact the sugarcane industry in Australia.
	2014

[34]	Climate ready sugarcane: Traits for adaptation to high CO2 levels. Final Report for Sugar Research Australia Project CPI018
	2014

[1]	Selected Studies in Geotechnics, Geo-informatics and Remote Sensing Advances in Science, Technology & Innovation, 2023 DOI:10.1007/978-3-031-43759-5_29

[2]	Potential for Sugarcane Production Under Current and Future Climates in South Africa: Sugar and Ethanol Yields, and Crop Water Use Sugar Tech, 2023 DOI:10.1007/s12355-022-01227-y

[3]	Climate change and Australia’s primary industries: factors hampering an effective and coordinated response International Journal of Biometeorology, 2022 DOI:10.1007/s00484-022-02265-7

[4]	The impact of climate change and climate extremes on sugarcane production GCB Bioenergy, 2021 DOI:10.1111/gcbb.12797

[5]	Global Optimization of Cultivar Trait Parameters in the Simulation of Sugarcane Phenology Using Gaussian Process Emulation Agronomy, 2021 DOI:10.3390/agronomy11071379

[6]	The impact of climate change and climate extremes on sugarcane production GCB Bioenergy, 2021 DOI:10.1111/gcbb.12797

[7]	Modeling Environmental Actions of Corporate Sustainable Activity: Evidence from Lithuania Central European Business Review, 2020 DOI:10.18267/j.cebr.223

[8]	Modeling Environmental Actions of Corporate Sustainable Activity: Evidence from Lithuania Central European Business Review, 2020 DOI:10.18267/j.cebr.223

[9]	Modeling Environmental Actions of Corporate Sustainable Activity: Evidence from Lithuania Central European Business Review, 2020 DOI:10.18267/j.cebr.223

[10]	Modeling Environmental Actions of Corporate Sustainable Activity: Evidence from Lithuania Central European Business Review, 2020 DOI:10.18267/j.cebr.223

[11]	Modeling Environmental Actions of Corporate Sustainable Activity: Evidence from Lithuania Central European Business Review, 2020 DOI:10.18267/j.cebr.223

[12]	Machine Learning and Data Mining in Aerospace Technology Studies in Computational Intelligence, 2020 DOI:10.1007/978-3-030-20212-5_6

[13]	Systems Modeling 2020 DOI:10.1007/978-981-15-4728-7_8

[14]	Accurate prediction of sugarcane yield using a random forest algorithm Agronomy for Sustainable Development, 2016 DOI:10.1007/s13593-016-0364-z

[15]	Quantification of the effects of climate warming and crop management on sugarcane phenology Climate Research, 2016 DOI:10.3354/cr01419

Follow SCIRP

	customer@scirp.org
	+86 18163351462(WhatsApp)
	1655362766

	Paper Publishing WeChat

Agricultural Sciences

Agricultural Sciences

Journals Menu

Home

About SCIRP

Service

Policies