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The cultivation of sugarcane (Saccharum officinarum L.) in the face of climate change requires robust strategies for managing pests, diseases, and weeds. This systematic review exposes critical deficiencies in current practices and underscores the need for climate-adaptive strategies. Climate change differentially influences pest behaviour, disease progression, and weed growth across various regions, yet the lack of region-specific responses impairs effective management. The review emphasizes the necessity for localized approaches that consider specific climatic conditions and the development of predictive models to anticipate pest and disease outbreaks. These models include Decision Support Systems (DSS), Support Vector Machines (SVM), Susceptible-Exposed-Infectious-Recovered (SEIR) models, Geographic Information Systems (GIS), Species Distribution Models (SDMs), Agricultural Production Systems sIMulator (APSIM), and Integrated Pest Management (IPM). Crucial strategies encompass integrated pest and disease management, adaptive breeding, precision agriculture, and ongoing innovation. Precision agriculture technologies, such as remote sensing and drones, enable early detection and prompt interventions. By adopting these adaptive measures and addressing existing research gaps, the sugarcane industry can bolster its resilience and maintain productivity amidst evolving climatic conditions. [ABSTRACT FROM AUTHOR]

The cultivation of sugarcane (Saccharum officinarum L.) in the face of climate change requires robust strategies for managing pests, diseases, and weeds. This systematic review exposes critical deficiencies in current practices and underscores the need for climate-adaptive strategies. Climate change differentially influences pest behaviour, disease progression, and weed growth across various regions, yet the lack of region-specific responses impairs effective management. The review emphasizes the necessity for localized approaches that consider specific climatic conditions and the development of predictive models to anticipate pest and disease outbreaks. These models include Decision Support Systems (DSS), Support Vector Machines (SVM), Susceptible-Exposed-Infectious-Recovered (SEIR) models, Geographic Information Systems (GIS), Species Distribution Models (SDMs), Agricultural Production Systems sIMulator (APSIM), and Integrated Pest Management (IPM). Crucial strategies encompass integrated pest and disease management, adaptive breeding, precision agriculture, and ongoing innovation. Precision agriculture technologies, such as remote sensing and drones, enable early detection and prompt interventions. By adopting these adaptive measures and addressing existing research gaps, the sugarcane industry can bolster its resilience and maintain productivity amidst evolving climatic conditions.Systematic review registrationhttps://www.bmj.com/content/372/bmj.n71.