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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Interpretable machine learning methods to explain on-farm yield variability of high productivity wheat in Northwest India
Hari Sankar Nayak, ‪João Vasco Silva, C.M. Parihar, et al.
Field Crops Research (2022) Vol. 287, pp. 108640-108640
Open Access | Times Cited: 33

Showing 1-25 of 33 citing articles:

Interpretability of deep learning models for crop yield forecasting
Dilli Paudel, Allard de Wit, Hendrik Boogaard, et al.
Computers and Electronics in Agriculture (2023) Vol. 206, pp. 107663-107663
Open Access | Times Cited: 55
Machine Learning in Sustainable Agriculture: Systematic Review and Research Perspectives
Juan Botero-Valencia, Vanessa García Pineda, Alejandro Valencia-Arías, et al.
Agriculture (2025) Vol. 15, Iss. 4, pp. 377-377
Open Access | Times Cited: 2
Exploring sludge yield patterns through interpretable machine learning models in China's municipal wastewater treatment plants
Y. Hu, Renke Wei, Ke Yu, et al.
Resources Conservation and Recycling (2024) Vol. 204, pp. 107467-107467
Closed Access | Times Cited: 10
Predicting carob tree physiological parameters under different irrigation systems using Random Forest and Planet satellite images
Simone Pietro Garofalo, Vincenzo Giannico, Beatriz Lorente, et al.
Frontiers in Plant Science (2024) Vol. 15
Open Access | Times Cited: 10
Prediction of sugar beet yield and quality parameters with varying nitrogen fertilization using ensemble decision trees and artificial neural networks
Ivana Varga, Dorijan Radočaj, Mladen Jurišić, et al.
Computers and Electronics in Agriculture (2023) Vol. 212, pp. 108076-108076
Closed Access | Times Cited: 21
Big data, small explanatory and predictive power: Lessons from random forest modeling of on-farm yield variability and implications for data-driven agronomy
‪João Vasco Silva, Joost van Heerwaarden, Pytrik Reidsma, et al.
Field Crops Research (2023) Vol. 302, pp. 109063-109063
Open Access | Times Cited: 13
Evaluating machine learning models and identifying key factors influencing spatial maize yield predictions in data intensive farm management
S. Maseko, Michael van der Laan, Eyob Habte Tesfamariam, et al.
European Journal of Agronomy (2024) Vol. 157, pp. 127193-127193
Open Access | Times Cited: 4
Paving the Roadmap for XAI and IML in Healthcare: Data-Driven Discoveries and the FIXAIH Framework
Saeed M. Alghamdi, Rashid Mehmood, Fahad Alqurashi, et al.
(2025)
Closed Access
Ensuring sustainable crop production when yield gaps are small: A data-driven integrated assessment for wheat farms in Northwest India
Hari Sankar Nayak, ‪João Vasco Silva, C.M. Parihar, et al.
European Journal of Agronomy (2025) Vol. 164, pp. 127492-127492
Open Access
Developing a modular and generalizable modeling framework for wheat yield prediction
Dehao Zhao
(2025)
Closed Access
Predicting and Mapping the Phosphorus Adsorption Maximum and Phosphorus Adsorption Affinity Constant at Regional Scale
Yu Gu, Gerard H. Ros, Qichao Zhu, et al.
European Journal of Soil Science (2025) Vol. 76, Iss. 1
Open Access
A comparison of physics‐based, data‐driven, and hybrid modeling approaches for rice phenology prediction
Jin Yu, Yifan Zhao, Guoqing Lei, et al.
Agronomy Journal (2025) Vol. 117, Iss. 1
Closed Access
Improving Wheat Yield Prediction with Multi-Source Remote Sensing Data and Machine Learning in Arid Regions
Aamir Raza, Muhammad Adnan Shahid, Muhammad Zaman, et al.
Remote Sensing (2025) Vol. 17, Iss. 5, pp. 774-774
Open Access
Spectral estimation of the aboveground biomass of cotton under water–nitrogen coupling conditions
S. Qiao, Jiaqiang Wang, Frank Yonghong Li, et al.
Plant Methods (2025) Vol. 21, Iss. 1
Open Access
Suitability assessment of film mulching on maize production in Northwest China: Integrating meta-analysis with machine learning
Juzhen Xu, Bowei Duan, Yanbo Wang, et al.
Field Crops Research (2025) Vol. 328, pp. 109919-109919
Closed Access
Interpretable machine learning for evaluating risk factors of freeway crash severity
Seyed Alireza Samerei, Kayvan Aghabayk
International Journal of Injury Control and Safety Promotion (2024) Vol. 31, Iss. 3, pp. 534-550
Closed Access | Times Cited: 3
Consistency and uncertainty of remote sensing-based approaches for regional yield gap estimation: A comprehensive assessment of process-based and data-driven models
Jingwen Wang, Jinsong Chen, Jiahua Zhang, et al.
Field Crops Research (2023) Vol. 302, pp. 109088-109088
Closed Access | Times Cited: 7
A machine learning modeling framework for Triticum turgidum subsp. durum Desf. yield forecasting in Italy
Marco Fiorentini, Calogero Schillaci, Michele Denora, et al.
Agronomy Journal (2022) Vol. 116, Iss. 3, pp. 1050-1070
Open Access | Times Cited: 11
Integrating Climate and Satellite Data for Multi-Temporal Pre-Harvest Prediction of Head Rice Yield in Australia
Allister Clarke, Darren Yates, Christopher Blanchard, et al.
Remote Sensing (2024) Vol. 16, Iss. 10, pp. 1815-1815
Open Access | Times Cited: 2
Crop recommendation and forecasting system for Maharashtra using machine learning with LSTM: a novel expectation-maximization technique
Yashashree Mahale, Nida Khan, Kunal Kulkarni, et al.
Discover Sustainability (2024) Vol. 5, Iss. 1
Open Access | Times Cited: 2
Improving rice yield and water productivity in dry climatic zones of West Africa: Season-specific strategies
Jean‐Martial Johnson, M. Becker, Elliott Ronald Dossou-Yovo, et al.
Field Crops Research (2024) Vol. 316, pp. 109519-109519
Open Access | Times Cited: 2
Context-dependent agricultural intensification pathways to increase rice production in India
Hari Sankar Nayak, Andrew J. McDonald, Virender Kumar, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 2
Response of lowland rice to phosphate amendments in three acidics agroecological zones of Côte d'Ivoire: Man-Gagnoa-Bouaké
Wondouet Hippolyte Kpan, Affi Jeanne Bongoua-Devisme, Konan-Kan Hippolyte Kouadio, et al.
International Journal of Environment Agriculture and Biotechnology (2023) Vol. 8, Iss. 5, pp. 135-144
Open Access | Times Cited: 6
Climate change expected to increase yield of spring cereals and reduce yield of winter cereals in the Western Siberian grain belt
Anton A. Goncharov, Taras A. Safonov, Alexander M. Malko, et al.
Field Crops Research (2023) Vol. 302, pp. 109038-109038
Open Access | Times Cited: 5
A machine learning approach is effective to elucidate yield-limiting factors of irrigated lowland rice under heterogeneous growing conditions and management practices
Vololonirina Raharimanana, Tomoaki Yamaguchi, Yasuhiro Tsujimoto, et al.
Field Crops Research (2023) Vol. 304, pp. 109170-109170
Closed Access | Times Cited: 4
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