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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:

EnvRtype: a software to interplay enviromics and quantitative genomics in agriculture
Germano Costa‐Neto, Giovanni Galli, Humberto Fanelli Carvalho, et al.
G3 Genes Genomes Genetics (2021) Vol. 11, Iss. 4
Open Access | Times Cited: 87

Showing 1-25 of 87 citing articles:

The Modern Plant Breeding Triangle: Optimizing the Use of Genomics, Phenomics, and Enviromics Data
José Crossa, Roberto Fritsche‐Neto, Osval A. Montesinos‐López, et al.
Frontiers in Plant Science (2021) Vol. 12
Open Access | Times Cited: 188
Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction
Yunbi Xu, Xingping Zhang, Huihui Li, et al.
Molecular Plant (2022) Vol. 15, Iss. 11, pp. 1664-1695
Open Access | Times Cited: 151
Breeding crops for drought-affected environments and improved climate resilience
Mark Cooper, Carlos D. Messina
The Plant Cell (2022) Vol. 35, Iss. 1, pp. 162-186
Open Access | Times Cited: 85
GIS-FA: an approach to integrating thematic maps, factor-analytic, and envirotyping for cultivar targeting
Maurício dos Santos Araújo, Saulo Fabrício da Silva Chaves, Luíz Antônio dos Santos Dias, et al.
Theoretical and Applied Genetics (2024) Vol. 137, Iss. 4
Open Access | Times Cited: 9
Satellite-enabled enviromics to enhance crop improvement
Rafael T Resende, Lee T. Hickey, Cibele Hummel do Amaral, et al.
Molecular Plant (2024) Vol. 17, Iss. 6, pp. 848-866
Open Access | Times Cited: 8
Can We Harness “Enviromics” to Accelerate Crop Improvement by Integrating Breeding and Agronomy?
Mark Cooper, Carlos D. Messina
Frontiers in Plant Science (2021) Vol. 12
Open Access | Times Cited: 50
Optimizing Plant Breeding Programs for Genomic Selection
Lance F. Merrick, Andrew W. Herr, Karansher Singh Sandhu, et al.
Agronomy (2022) Vol. 12, Iss. 3, pp. 714-714
Open Access | Times Cited: 37
Genome–Environment Associations, an Innovative Tool for Studying Heritable Evolutionary Adaptation in Orphan Crops and Wild Relatives
Andrés J. Cortés, Felipe López-Hernández, Matthew W. Blair
Frontiers in Genetics (2022) Vol. 13
Open Access | Times Cited: 37
Genome and Environment Based Prediction Models and Methods of Complex Traits Incorporating Genotype × Environment Interaction
José Crossa, Osval A. Montesinos‐López, Paulino Pérez‐Rodríguez, et al.
Methods in molecular biology (2022), pp. 245-283
Open Access | Times Cited: 36
Multimodal deep learning methods enhance genomic prediction of wheat breeding
Abelardo Montesinos‐López, Carolina Rivera, Francisco Pinto, et al.
G3 Genes Genomes Genetics (2023) Vol. 13, Iss. 5
Open Access | Times Cited: 17
Deciphering temporal growth patterns in maize: integrative modeling of phenotype dynamics and underlying genomic variations
Alper Adak, Seth C. Murray, Jacob D. Washburn
New Phytologist (2024) Vol. 242, Iss. 1, pp. 121-136
Open Access | Times Cited: 7
Enviromic Assembly Increases Accuracy and Reduces Costs of the Genomic Prediction for Yield Plasticity in Maize
Germano Costa‐Neto, José Crossa, Roberto Fritsche‐Neto
Frontiers in Plant Science (2021) Vol. 12
Open Access | Times Cited: 35
Enviromic prediction is useful to define the limits of climate adaptation: A case study of common bean in Brazil
Alexandre Bryan Heinemann, Germano Costa‐Neto, Roberto Fritsche‐Neto, et al.
Field Crops Research (2022) Vol. 286, pp. 108628-108628
Open Access | Times Cited: 26
Envirome-wide associations enhance multi-year genome-based prediction of historical wheat breeding data
Germano Costa‐Neto, Leonardo Crespo‐Herrera, Nick Fradgley, et al.
G3 Genes Genomes Genetics (2022) Vol. 13, Iss. 2
Open Access | Times Cited: 25
Multi-trait selection for mean performance and stability of maize hybrids in mega-environments delineated using envirotyping techniques
Haiwang Yue, Tiago Olivoto, Junzhou Bu, et al.
Frontiers in Plant Science (2022) Vol. 13
Open Access | Times Cited: 24
Extending Finlay–Wilkinson regression with environmental covariates
Hans‐Peter Piepho, Justin Blancon
Plant Breeding (2023) Vol. 142, Iss. 5, pp. 621-631
Open Access | Times Cited: 13
Assessing the role of genotype by environment interaction as determinants of maize grain yield and lodging resistance
Haiwang Yue, Tiago Olivoto, Junzhou Bu, et al.
BMC Plant Biology (2025) Vol. 25, Iss. 1
Open Access
Accurate genomic prediction for grain yield and grain moisture content of maize hybrids using multi‐environment data
Jingxin Wang, Liwei Liu, Kunhui He, et al.
Journal of Integrative Plant Biology (2025)
Closed Access
Using soybean historical field trial data to study genotype by environment variation and identify mega‐environments with the integration of genetic and non‐genetic factors
Matheus Dalsente Krause, Kaio Olímpio das Graças Dias, Asheesh K. Singh, et al.
Agronomy Journal (2025) Vol. 117, Iss. 1
Open Access
Exploring the Interplay between Drought Resilience and Yield in Maize Hybrids for Drought-Prone Regions
S. S. Bal, Alper Adak, Şekip ERDAL
Tropical Plant Biology (2025) Vol. 18, Iss. 1
Open Access
Optimizing Genomic-Enabled Prediction in Small-Scale Maize Hybrid Breeding Programs: A Roadmap Review
Roberto Fritsche‐Neto, Giovanni Galli, Karina Lima Reis Borges, et al.
Frontiers in Plant Science (2021) Vol. 12
Open Access | Times Cited: 27
Space Breeding: The Next-Generation Crops
Tapan Kumar Mohanta, Awdhesh Kumar Mishra, Yugal Kishore Mohanta, et al.
Frontiers in Plant Science (2021) Vol. 12
Open Access | Times Cited: 27
Genomic prediction for complex traits across multiples harvests in alfalfa ( Medicago sativa L.) is enhanced by enviromics
Cláudio Carlos Fernandes Filho, Mário Henrique Murad Leite Andrade, José Aírton Rodrigues Nunes, et al.
The Plant Genome (2023) Vol. 16, Iss. 2
Open Access | Times Cited: 11
Envirotypes applied to evaluate the adaptability and stability of wheat genotypes in the tropical region in Brazil
Cleiton Renato Casagrande, Henrique Caletti Mezzomo, Diana Jhulia Palheta de Sousa, et al.
Euphytica (2024) Vol. 220, Iss. 2
Open Access | Times Cited: 4
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
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