Modelling and mapping soil nutrient depletion in humid highlands of East Africa using ensemble machine learning: A case study from Rwanda-Reference-Cited by-同舟云学术

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Modelling and mapping soil nutrient depletion in humid highlands of East Africa using ensemble machine learning: A case study from Rwanda

Published:2022-10 Issue: Volume:217 Page:106499
ISSN:0341-8162
Container-title:CATENA
language:en
Short-container-title:CATENA

Author:

Uwiragiye Yves,Junior Yannick Ngaba Mbezele,Zhao Mengzhen,Elrys Ahmed S.,Heuvelink Gerard B.M.,Zhou Jianbin

Funder

National Key Research and Development Program of China

Higher Education Discipline Innovation Project

Special Funds for the Basic Research and Development Program in the Central Non-profit Research Institutes of China

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Earth-Surface Processes

Reference81 articles.

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2. Ayyadevara, V.K., 2018. Gradient Boosting Machine.In: Pro Machine Learning Algorithms. Apress, Berkeley, CA. 10.1007/978-1-4842-3564-5_6.

3. Land quality indicators for sustainable land management: proposed method for yield gap and soil nutrient balance;Bindraban;Agr Ecosyst Env.,2000

4. Emissions of N2O and NO from fertilized fields: Summary of available measurement data;Bouwman;Global Biogeochem. Cycles,2002

5. Determining and managing maize yield gaps in Rwanda;Bucagu;Food Secur,2020

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