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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:

Global crop yield forecasting using seasonal climate information from a multi-model ensemble
Toshichika Iizumi, Yonghee Shin, Wonsik Kim, et al.
Climate Services (2018) Vol. 11, pp. 13-23
Open Access | Times Cited: 109

Showing 1-25 of 109 citing articles:

Applying IoT Sensors and Big Data to Improve Precision Crop Production: A Review
Tarek Alahmad, Miklós Neményi, Anikó Nyéki
Agronomy (2023) Vol. 13, Iss. 10, pp. 2603-2603
Open Access | Times Cited: 52
Climate change impacts on agricultural suitability and yield reduction in a Mediterranean region
Sameh Kotb Abd‐Elmabod, Miriam Muñoz‐Rojas, António Jordán, et al.
Geoderma (2020) Vol. 374, pp. 114453-114453
Closed Access | Times Cited: 114
Integrated approaches to understanding and reducing drought impact on food security across scales
Xiaogang He, Lyndon Estes, Megan Konar, et al.
Current Opinion in Environmental Sustainability (2019) Vol. 40, pp. 43-54
Open Access | Times Cited: 94
A systematic review of local to regional yield forecasting approaches and frequently used data resources
Bernhard Schauberger, Jonas Jägermeyr, Christoph Gornott
European Journal of Agronomy (2020) Vol. 120, pp. 126153-126153
Open Access | Times Cited: 93
California Almond Yield Prediction at the Orchard Level With a Machine Learning Approach
Zhou Zhang, Yufang Jin, Бин Чэн, et al.
Frontiers in Plant Science (2019) Vol. 10
Open Access | Times Cited: 86
Climate-driven changes in the predictability of seasonal precipitation
Phong V. V. Le, James T. Randerson, Rebecca Willett, et al.
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 26
Climate Change and Food Systems
Alisher Mirzabaev, Lennart Olsson, Rachel Bezner Kerr, et al.
Springer eBooks (2023), pp. 511-529
Closed Access | Times Cited: 25
Perception of farmers on climate change and its impacts on agriculture across various altitudinal zones of Bhutan Himalayas
Ngawang Chhogyel, Lalit Kumar, Yadunath Bajgai, et al.
International Journal of Environmental Science and Technology (2020) Vol. 17, Iss. 8, pp. 3607-3620
Closed Access | Times Cited: 62
Agri-biomass supply chain optimization in north China: Model development and application
Juanjuan Wu, Jian Zhang, Weiming Yi, et al.
Energy (2021) Vol. 239, pp. 122374-122374
Closed Access | Times Cited: 48
Potential of remote sensing data-crop model assimilation and seasonal weather forecasts for early-season crop yield forecasting over a large area
Yi Chen, Fulu Tao
Field Crops Research (2021) Vol. 276, pp. 108398-108398
Closed Access | Times Cited: 48
Bottlenecks and opportunities in field-based high-throughput phenotyping for heat and drought stress
Nathan T. Hein, Ignacio A. Ciampitti, S. V. Krishna Jagadish
Journal of Experimental Botany (2021) Vol. 72, Iss. 14, pp. 5102-5116
Open Access | Times Cited: 45
Performance of a wheat yield prediction model and factors influencing the performance: A review and meta-analysis
Shirui Hao, Dongryeol Ryu, Andrew W. Western, et al.
Agricultural Systems (2021) Vol. 194, pp. 103278-103278
Closed Access | Times Cited: 44
A Geographically Weighted Random Forest Approach to Predict Corn Yield in the US Corn Belt
Shahid Nawaz Khan, Dapeng Li, Maitiniyazi Maimaitijiang
Remote Sensing (2022) Vol. 14, Iss. 12, pp. 2843-2843
Open Access | Times Cited: 36
Improving the Forecasting of Winter Wheat Yields in Northern China with Machine Learning–Dynamical Hybrid Subseasonal-to-Seasonal Ensemble Prediction
Junjun Cao, Huijing Wang, Jinxiao Li, et al.
Remote Sensing (2022) Vol. 14, Iss. 7, pp. 1707-1707
Open Access | Times Cited: 35
Global adaptation readiness and income mitigate sectoral climate change vulnerabilities
Samuel Asumadu Sarkodie, Maruf Yakubu Ahmed, Phebe Asantewaa Owusu
Humanities and Social Sciences Communications (2022) Vol. 9, Iss. 1
Open Access | Times Cited: 30
Time Series from Sentinel-2 for Organic Durum Wheat Yield Prediction Using Functional Data Analysis and Deep Learning
Adriano Mancini, Francesco Solfanelli, Luca Coviello, et al.
Agronomy (2024) Vol. 14, Iss. 1, pp. 109-109
Open Access | Times Cited: 7
Review article: Drought as a continuum – memory effects in interlinked hydrological, ecological, and social systems
Anne F. Van Loon, Sarra Kchouk, Alessia Matanó, et al.
Natural hazards and earth system sciences (2024) Vol. 24, Iss. 9, pp. 3173-3205
Open Access | Times Cited: 7
Review article: Drought as a continuum: memory effects in interlinked hydrological, ecological, and social systems
Anne F. Van Loon, Sarra Kchouk, Alessia Matanó, et al.
(2024)
Open Access | Times Cited: 6
Modeling the Global Sowing and Harvesting Windows of Major Crops Around the Year 2000
Toshichika Iizumi, Wonsik Kim, Motoki Nishimori
Journal of Advances in Modeling Earth Systems (2018) Vol. 11, Iss. 1, pp. 99-112
Open Access | Times Cited: 50
Comparing a Random Forest Based Prediction of Winter Wheat Yield to Historical Yield Potential
Yannik Roell, Amélie Beucher, Per Grau Møller, et al.
Agronomy (2020) Vol. 10, Iss. 3, pp. 395-395
Open Access | Times Cited: 40
Application of spatio-temporal data in site-specific maize yield prediction with machine learning methods
Anikó Nyéki, Csaba Kerepesi, Bálint Daróczy, et al.
Precision Agriculture (2021) Vol. 22, Iss. 5, pp. 1397-1415
Open Access | Times Cited: 34
Predicting Sugarcane Harvest Date and Productivity with a Drone-Borne Tri-Band SAR
Gian Oré, Marlon S. Alcântara, Juliana A. Góes, et al.
Remote Sensing (2022) Vol. 14, Iss. 7, pp. 1734-1734
Open Access | Times Cited: 23
Improving crop yield prediction accuracy by embedding phenological heterogeneity into model parameter sets
Simone Bregaglio, Fabrizio Ginaldi, Elisabetta Raparelli, et al.
Agricultural Systems (2023) Vol. 209, pp. 103666-103666
Open Access | Times Cited: 14
Predicting Soybean Yield at the Regional Scale Using Remote Sensing and Climatic Data
Alexey Stepanov, Konstantin Dubrovin, А. А. Сорокин, et al.
Remote Sensing (2020) Vol. 12, Iss. 12, pp. 1936-1936
Open Access | Times Cited: 35
County-scale crop yield prediction by integrating crop simulation with machine learning models
Saiara Samira Sajid, Mohsen Shahhosseini, Isaiah Huber, et al.
Frontiers in Plant Science (2022) Vol. 13
Open Access | Times Cited: 21
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