Weather data-centric prediction of maize non-stressed canopy temperature in semi-arid climates for irrigation management

Author:

Nakabuye Hope Njuki,Rudnick Daran R.,DeJonge Kendall C.,Ascough Katherine,Liang Wei-zhen,Lo Tsz Him,Franz Trenton E.,Qiao Xin,Katimbo Abia,Duan Jiaming

Funder

United States Department of Agriculture’s National Institute of Food and Agriculture

Water for Food Daugherty Global Institute

Publisher

Springer Science and Business Media LLC

Subject

Soil Science,Water Science and Technology,Agronomy and Crop Science

Reference67 articles.

1. Abyaneh HZ, Nia AM, Varkeshi MB et al (2011) Performance evaluation of ANN and ANFIS models for estimating garlic crop evapotranspiration. J Irrig Drain Eng 137(5):280–286. https://doi.org/10.1061/(asce)ir.1943-4774.0000298

2. Ahmed K, Sachindra DA, Shahid S et al (2020) Multi-model ensemble predictions of precipitation and temperature using machine learning algorithms. Atmos Res 236:104806. https://doi.org/10.1016/j.atmosres.2019.104806

3. Allen RG, Pereira LS, Smith M et al (2005) FAO-56 Dual crop coefficient method for estimating evaporation from soil and application extensions. J Irrig Drain Eng 131(1):2–13. https://doi.org/10.1061/(asce)0733-9437(2005)131:1(2)

4. Allen RG, Pereira LS, Raes D et al (1998) Crop evapotranspiration guidelines for computing crop water requirements. In FAO Irrigation and drainage Paper 56. FAO, Food and Agriculture Organization of the United Nations, Rome https://www.fao.org/3/x0490e/x0490e00.htm

5. Andrade MA, Evett SR, O’Shaughnessy SA (2018) Machine learning algorithms applied to the forecasting of crop water stress indicators. Proc Irrigation Association Technical Conference

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