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Mitigating crop modeling uncertainties through machine learning in drylands.

• Authors : Nouri M; Soil and Water Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran. .; Veysi S

• Source: Scientific reports [Sci Rep] 2025 Nov 28; Vol. 15 (1), pp. 42663. Date of Electronic Publication: 2025 Nov 28.Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN:

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Integrated use of regional weather forecasting and crop modeling for water stress assessment on rice yield.

• Authors : Rajasivaranjan T; Indian Institute of Remote Sensing, Dehradun, 248001, India.; Anandhi A

Subjects: Oryza*; Agriculture ; Climate Change

• Source: Scientific reports [Sci Rep] 2022 Oct 10; Vol. 12 (1), pp. 16985. Date of Electronic Publication: 2022 Oct 10.Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN:

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Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt.

• Authors : Shahhosseini M; Department of Industrial and Manufacturing Systems Engineering, Iowa State University, Ames, IA, USA.; Hu G

Subjects: Crop Production* ; Machine Learning*; Zea mays/Zea mays/Zea mays/*growth & development

• Source: Scientific reports [Sci Rep] 2021 Jan 15; Vol. 11 (1), pp. 1606. Date of Electronic Publication: 2021 Jan 15.Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN:

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From crop specific to variety specific in crop modeling for the smart farm: A case study with blueberry.

• Authors : Han GD; Department of Practical Course Education, Cheongju National University of Education, Cheongju, Republic of Korea.; Choi JM

Subjects: Blueberry Plants*; Agriculture/Agriculture/Agriculture/methods ; Artificial Intelligence

• Source: PloS one [PLoS One] 2022 Aug 30; Vol. 17 (8), pp. e0273845. Date of Electronic Publication: 2022 Aug 30 (Print Publication: 2022).Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet

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A time-dependent parameter estimation framework for crop modeling.

• Authors : Akhavizadegan F; Department of Industrial and Manufacturing Systems Engineering, Iowa State University, Ames, IA, 50011, USA. .; Ansarifar J

• Source: Scientific reports [Sci Rep] 2021 Jun 01; Vol. 11 (1), pp. 11437. Date of Electronic Publication: 2021 Jun 01.Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN:

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Integrating Plant Science and Crop Modeling: Assessment of the Impact of Climate Change on Soybean and Maize Production.

• Authors : Fodor N; Centre for Plant Sciences, School of Biology, Faculty of Biological Sciences, University of Leeds, LS2 9JT Leeds, UK.; Centre for Agricultural Research, Hungarian Academy of Sciences, 2462 Martonvásár Brunszvik u. 2., Hungary.

Subjects: Climate Change* ; Models, Biological*; Crops, Agricultural/Crops, Agricultural/Crops, Agricultural/*physiology

• Source: Plant & cell physiology [Plant Cell Physiol] 2017 Nov 01; Vol. 58 (11), pp. 1833-1847.Publisher: Oxford University Press Country of Publication: Japan NLM ID: 9430925 Publication Model: Print Cited Medium: Internet ISSN: 1471-9053

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Combining Limited Multiple Environment Trials Data with Crop Modeling to Identify Widely Adaptable Rice Varieties.

• Authors : Li T; Crop and Environmental Sciences Division, International Rice Research Institute, Los Baños, Laguna, Philippines.; Ali J

Subjects: Models, Biological*; Crop Production/Crop Production/Crop Production/*methods ; Crops, Agricultural/Crops, Agricultural/Crops, Agricultural/*growth & development

• Source: PloS one [PLoS One] 2016 Oct 10; Vol. 11 (10), pp. e0164456. Date of Electronic Publication: 2016 Oct 10 (Print Publication: 2016).Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet

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Assessment of the Potential Impacts of Wheat Plant Traits across Environments by Combining Crop Modeling and Global Sensitivity Analysis.

• Authors : Casadebaig P; INRA, UMR1248 AGIR, 31326 Castanet-Tolosan, France.; Zheng B

Subjects: Computer Simulation* ; Models, Biological* ; Quantitative Trait, Heritable*

• Source: PloS one [PLoS One] 2016 Jan 22; Vol. 11 (1), pp. e0146385. Date of Electronic Publication: 2016 Jan 22 (Print Publication: 2016).Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet

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Evaluation of crop phenology using remote sensing and decision support system for agrotechnology transfer.

• Authors : Amin NU; Department of Meteorology, COMSATS University Islamabad (CUI), Park Road, Tarlai Kalan, Islamabad, 45550, Pakistan.; Islam F

Subjects: Remote Sensing Technology*/Remote Sensing Technology*/Remote Sensing Technology*/methods ; Crops, Agricultural*/Crops, Agricultural*/Crops, Agricultural*/growth & development ; Agriculture*/Agriculture*/Agriculture*/methods

• Source: Scientific reports [Sci Rep] 2025 Apr 04; Vol. 15 (1), pp. 11582. Date of Electronic Publication: 2025 Apr 04.Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN:

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The FIP 1.0 Data Set: Highly resolved annotated image time series of 4,000 wheat plots grown in 6 years.

• Authors : Roth L; ETH Zürich, Institute of Agricultural Sciences, 8092 Zürich, Switzerland.; Boss M

Subjects: Triticum*/Triticum*/Triticum*/genetics ; Triticum*/Triticum*/Triticum*/growth & development; Crops, Agricultural/Crops, Agricultural/Crops, Agricultural/genetics

• Source: GigaScience [Gigascience] 2025 Jan 06; Vol. 14.Publisher: Oxford University Press Country of Publication: United States NLM ID: 101596872 Publication Model: Print Cited Medium: Internet ISSN:

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