Enhancing irrigation water management based on ETo prediction using machine learning to mitigate climate change-Reference-Cited by-同舟云学术

Enhancing irrigation water management based on ETo prediction using machine learning to mitigate climate change

Published:2024-05-09 Issue:1 Volume:10 Page:
ISSN:2331-1932
Container-title:Cogent Food & Agriculture
language:en
Short-container-title:Cogent Food & Agriculture

Author:

Youssef Mohamed A.¹,Peters R. Troy²,El-Shirbeny Mohammed³⁴,Abd-ElGawad Ahmed M.⁵,Rashad Younes M.⁶^ORCID,Hafez Mohamed⁷,Arafa Yasser¹

Affiliation:

1. Department of Agricultural Engineering, Faculty of Agriculture, Ain Shams University, Cairo, Egypt

2. Department of Biological Systems Engineering, Washington State University, Pullman, Washington, USA

3. National Authority for Remote Sensing and Space Sciences (NARSS), Cairo, Egypt

4. Arab Organization for Agricultural Development (AOAD), Cairo, Egypt

5. Plant Production Department, College of Food & Agriculture Sciences, King Saud University, Riyadh, Saudi Arabia

6. Plant Protection and Biomolecular Diagnosis Department, Arid Lands Cultivation Research Institute (ALCRI), City of Scientific Research and Technological Applications (SRTA-City), New Borg El-Arab, Egypt

7. Land and Water Technologies Department, Arid Lands Cultivation Research Institute (ALCRI), City of Scientific Research and Technological Applications (SRTA-City), New Borg El-Arab, Egypt

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/23311932.2024.2348697

Reference68 articles.

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4. High-resolution global grids of revised Priestley–Taylor and Hargreaves–Samani coefficients for assessing ASCE-standardized reference crop evapotranspiration and solar radiation

5. Estimation of green and blue water evapotranspiration using machine learning algorithms with limited meteorological data: A case study in Amu Darya River Basin, Central Asia

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