Crop modelling applied to Portuguese viticulture: a brief overview

Traditional viticulture is becoming increasingly insufficient to deal with the pressure from climate changes and market competitiveness. Due to these challenges, efficient modelling is needed to increase the efficiency and modernization of the sector. This work presents a review of the most relevant research on grapevine crop modelling, including an assessment of statistical and dynamic modelling, as well as machine learning techniques and applications. A brief overview of the viticultural sector (national and international) is given, highlighting the declining vineyard areas and economic fragmentation, particularly in Portugal. Drawing from agricultural systems science, recent studies on climate change effects on European viticulture, and advancements in dynamic and machine learning models, the research aims to address the complexities of vineyard management and climate change. The importance of crop models in viticulture is evidenced, and opportunities for future improvements are presented, such as enhanced phenological modelling and precision viticulture techniques. Challenges, including data quality issues and the need for region-specific models, are also identified. The resulting impact of overcoming these challenges can have a very significant influence on the long-term efficiency, productivity, resilience and sustainability of the Portuguese wine industry. The review concludes that the future of viticulture will rely on integrating advanced modelling techniques with traditional practices, emphasizing the need for interdisciplinary collaboration and real-time data processing to ensure adaptability in an uncertain climate.