{
  "abstract": "Accurate prediction of Potato crop coefficient ( K c ) is an essential factor for effective irrigation management, as it directly impacts the percentage of crop water use. Capturing dynamic patterns from agro-meteorological data for K c prediction presents a significant challenges due to the complex structure of the agro-meteorological data. This paper proposes a probabilistic dynamic graph deep learning (PDGDL) model for K c prediction in the Province of Prince Edward Island (PEI), Canada. The proposed PDGDL model employs an encoder and decoder to learn the dynamic graph representations, which involves three layers: causal layer, Reasoning Layer, and output layer. The PDGDL model learns the influence of variables from historical records to the future period to form the final K c prediction. Extensive experiments are conducted on two-year experimental data (2023-2024) collected in PEI from three fields representing three types of soils. Numerous statistical and visual metrics are applied to evaluate the proposed model against seven benchmark models, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Networks (CNN) based a Bidirectional LSTM (CBiLSTM), BiLSTM, CNN based on GRU (CGRU), and graph deep learning models, Graph neural network (GNN), and Dynamic graph neural network (DGNN). The results demonstrated the ability of the proposed PDGDL model to predict K c in different soil types. It achieved the Root Mean Square Errors (RMSE) of 0.62, 0.63, 0.61, Mean Absolute Errors (MAE) of 0.312, 0.320, 0.301, and the coefficients of determination (R 2 ) of 0.995, 0.984, 0.972 for three fields. These improvements showed the ability of the proposed PDGDL model to effectively handle complex and non-linear patterns of K c , delivering accurate predictive results compared to other graph deep learning models, and offering helpful support for decision making in the agriculture sector.",
  "abstract_shown": true,
  "affiliations": [
    "Canadian Centre for Climate Change and Adaptation, University of Prince Edward Island, St Peter's Bay, PE, Canada",
    "School of Business, Law, Humanities, and Pathways, University of Southern Queensland, QLD, 4300, Australia; Scientific Research Centre, Al-Ayen Iraqi University, Thi-Qar, Iraq; Corresponding author. School of Business, Law, Humanities, and Pathways, University of Southern Queensland, QLD 4300, Australia.",
    "Canadian Centre for Climate Change and Adaptation, University of Prince Edward Island, St Peter's Bay, PE, Canada; Faculty of Sustainable Design Engineering, University of Prince Edward Island, Charlottetown, PE, Canada; Corresponding author. Canadian Centre for Climate Change and Adaptation, University of Prince Edward Island, St Peters Bay, PE, Canada.",
    "Department of Civil Engineering, McMaster University, Hamilton, ON, Canada",
    "Faculty of Science and Technology, Athabasca University, Athabasca, AB, Canada",
    "Citrus Research and Education Centre, University of Florida, FL, USA",
    "Civil and Environmental Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 9,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Mohammed Diykh"
    },
    {
      "affs": [
        1
      ],
      "name": "Mumtaz Ali"
    },
    {
      "affs": [
        2
      ],
      "name": "Aitazaz A. Farooque"
    },
    {
      "affs": [
        0
      ],
      "name": "Saad Javed Cheema"
    },
    {
      "affs": [
        0
      ],
      "name": "Mehdi Jamei"
    },
    {
      "affs": [
        3
      ],
      "name": "Zoe Li"
    },
    {
      "affs": [
        4
      ],
      "name": "Junye Wang"
    },
    {
      "affs": [
        5
      ],
      "name": "Arnold W. Schumann"
    },
    {
      "affs": [
        6
      ],
      "name": "Zaher Mundher Yaseen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/c55a7nvck7ht0.bib",
  "cited_by": 0,
  "doi": "10.1016/j.jafr.2026.103292",
  "fulltext_url": "http://www.sciencedirect.com/science/article/pii/S2666154326006630",
  "id": "c55a7nvck7ht0",
  "issns": [
    "2666-1543"
  ],
  "landing_url": "https://doi.org/10.1016/j.jafr.2026.103292",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "S1-972"
    ],
    [
      "lcc",
      "TX341-641"
    ],
    [
      "openalex",
      "1110|Smart Agriculture and AI"
    ]
  ],
  "openalex": "W7213507609",
  "pdf_url": "https://www.sciencedirect.com/science/article/pii/S2666154326006630/pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-30",
  "publisher": "Elsevier",
  "ris": "https://research.draconitic.com/paper/c55a7nvck7ht0.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:44:57Z",
      "native_id": "b5222b5b49af4c90a0edb3dae822523b",
      "source": "doaj",
      "url": "https://doi.org/10.1016/j.jafr.2026.103292"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:1110|Smart Agriculture and AI",
      "subfield": "plant-biology"
    }
  ],
  "title": "Potato crop coefficient (",
  "title_text": "Potato crop coefficient (",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:44:57Z",
  "url": "https://research.draconitic.com/paper/c55a7nvck7ht0",
  "venue": "Journal of Agriculture and Food Research",
  "versions": []
}