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Using boosted tree regression and artificial neural networks to forecast upland rice yield under climate change in Sahel

  • Published:2019-11 Issue: Volume:166 Page:105031
  • ISSN:0168-1699
  • Container-title:Computers and Electronics in Agriculture
  • language:en
  • Short-container-title:Computers and Electronics in Agriculture

Author:

Zhang Lei,Traore Seydou,Ge Jiankun,Li Yanbin,Wang Shunsheng,Zhu Ge,Cui Yuanlai,Fipps Guy

Funder

National Natural Science Foundation of China

Scientific and Technological Innovation Team for Colleges and Universities in Henan Province

Publisher

Elsevier BV

Subject

Horticulture,Computer Science Applications,Agronomy and Crop Science,Forestry

Reference49 articles.

1. Multilayer perceptron neural network for downscaling rainfall in arid region: A case study of Baluchistan;Ahmed;Pakistan. J. Earth Syst. Sci.,2015

2. A stochastic modelling approach for real-time forecasting of winter wheat yield;Bannayan;Field Crop. Res.,1999

3. Effect of climate change on rice yield at Kharagpur, West Bengal;Bhattacharya;Int. J. Food, Agric. Vet. Sci.,2013

4. Statistical downscaling of daily temperatures in the NW Iberian Peninsula from global climate models: validation and future scenarios;Brands;Clim. Res.,2011

5. An improved model to simulate rice yield;Confalonieri;Agron. Sustain. Dev.,2009

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