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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:

How process-based modeling can help plant breeding deal with G x E x M interactions
Amir Hajjarpoor, William C. Nelson, Vincent Vadez
Field Crops Research (2022) Vol. 283, pp. 108554-108554
Open Access | Times Cited: 15

Showing 15 citing articles:

Crop traits and production under drought
Vincent Vadez, Alexandre Grondin, Karine Chenu, et al.
Nature Reviews Earth & Environment (2024) Vol. 5, Iss. 3, pp. 211-225
Closed Access | Times Cited: 50
On-farm soybean genetic progress and yield stability during the early 21st century: A case study of a commercial breeding program in Argentina and Brazil
Lucas J. Abdala, María E. Otegui, Guido Di Mauro
Field Crops Research (2024) Vol. 308, pp. 109277-109277
Closed Access | Times Cited: 6
Stability models regulate the adaptation of male sterility-based chilli hybrids for agro-ecologically diverse regions
Vivek Singh, Akhilesh Sharma, Nimit Kumar, et al.
Research Square (Research Square) (2025)
Closed Access
An enhanced genetics × environment × management framework for yield intensification
R. E. Moss, T. Fairhurst, Patricio Grassini
Nature Food (2025)
Closed Access
Improving drought tolerance in maize: Tools and techniques
Michael S. McMillen, Anthony A. Mahama, Julia Sibiya, et al.
Frontiers in Genetics (2022) Vol. 13
Open Access | Times Cited: 22
Harnessing crop models and machine learning for a spatial-temporal characterization of irrigated rice breeding environments in Brazil
Alexandre Bryan Heinemann, Germano Costa‐Neto, David Henriques da Matta, et al.
Field Crops Research (2024) Vol. 315, pp. 109452-109452
Closed Access | Times Cited: 4
Relationships between combined and individual field crops’ biomass and planting density
Shmulik P. Friedman
Field Crops Research (2023) Vol. 305, pp. 109188-109188
Closed Access | Times Cited: 10
Boosting genome-wide association power and genomic prediction accuracy for date palm fruit traits with advanced statistics
Abdulqader Jighly
Plant Science (2024) Vol. 344, pp. 112110-112110
Closed Access | Times Cited: 1
Characterization of common bean production regions in Brazil using machine learning techniques
Ludmilla Ferreira Justino, Alexandre Bryan Heinemann, David Henriques da Matta, et al.
Agricultural Systems (2024) Vol. 224, pp. 104237-104237
Closed Access | Times Cited: 1
The causal arrows ̶ from genotype, environment and management to plant phenotype ̶ are double headed
Víctor O. Sadras, Peter Hayman
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access
Improving crop productivity through data-driven optimization and hybrid deep learning-based approaches
Zahra Khalilzadeh
(2024)
Open Access
Sorghum Environment Characterization and G × M Modeling Toolbox
Sunita Choudhary, Kaliamoorthy Sivasakthi
(2024), pp. 433-453
Closed Access
The causal arrows from genotype, environment and management to plant phenotype are double headed
Víctor O. Sadras, Peter Hayman
Journal of Experimental Botany (2024)
Open Access
A hybrid deep learning-based approach for optimal genotype by environment selection
Zahra Khalilzadeh, Motahareh Kashanian, Saeed Khaki, et al.
Frontiers in Artificial Intelligence (2024) Vol. 7
Open Access
Soil recovery in cacao and coffee agroforestry systems : Litter, roots, carbon, and water
Danny Dwi Saputra
(2023)
Open Access
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