Knowledge and Opportunities for Managing Plant-Parasitic Nematodes Using Decision Intelligence

Plant-parasitic nematodes cause significant yield losses and economic damage to crops worldwide. Traditional management strategies are costly and environmentally unsustainable. This study introduces decision intelligence (DI) to enhance nematode management in sweetpotato cultivation. DI integrates decision analysis, artificial intelligence, and machine learning to support decision-making in complex environments. Through expert elicitations with researchers and extension agents, causal decision diagrams and causal decision models were developed for nematode management. These models were refined and tested at a field day event, revealing critical actions, intermediates, and external factors that influence outcomes, with profitability identified as the key outcome. Key intermediates included nematode soil populations in subsequent years, influenced by actions such as fumigation, planting a susceptible target crop (sweetpotatoes), or planting a non-susceptible rotational crop. External factors included soil temperature, moisture, and the costs associated with different management choices. Development of the causal decision model highlighted gaps in existing studies, particularly the need for data linking pesticide soil concentrations to nematode reduction under varying soil conditions. This research demonstrates the potential benefits of DI in plant pathology, including the ability to (i) engage stakeholders early in the modeling process, (ii) visualize complex domain-specific problems for interdisciplinary teams, and (iii) provide immediate value in understanding decision-making processes to both stakeholders and researchers. Overall, DI offers a promising approach to enhancing agricultural decision-making and has potential applications in plant pathology. [Formula: see text] Copyright © 2025 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .