It is crucial to precisely evaluate the current disease risk by integrating various pieces of information. This integration can be exemplified though the disease triangle, a well-established concept in plant pathology. According to this conceptual model, the actual development of a disease requires the simultaneous presence of three critical factors: a susceptible host, a virulent pathogen, and an environment conducive to disease development.<\/p>\n\n\n
![\"\"](\"https:\/\/vintel-itk.com\/wp-content\/uploads\/2024\/03\/triangle-Maladie-Eng.jpg\")
<\/figure>\n<\/div>\n\n\nWhile it’s generally easy to determine the presence of a pathogen in a given region, assessing plant susceptibility and climatic propensity to disease can be more complex. Each disease reacts to specific weather conditions and attacks the plant during specific windows of vulnerability, often associated with particular phenological phases. For example, grapevines are susceptible to powdery mildew from the stage when the leaves are half-spread to the stage when the berries become resistant to penetration by the fungus, before the ripening process begins. So, to assess the real risk of disease, it is necessary to take into account not only the impact of weather conditions on powdery mildew, but also the vulnerability of vines at different phenological stages.<\/p>\n\n\n\n The complexity of plant-environment-pathogen interactions can pose challenges in evaluating actual disease risk and making rapid, effective phytosanitary decisions. To address these issues, precision viticulture utilizes tools that simultaneously consider all contributing factors. One effective approach is the integration of plant phenological models with disease models within a Decision Support System (DSS). <\/p>\n\n\n\n This method involves simulating the interaction between disease development and plant vulnerability. This combined modeling approach allows an accurate assessment of risk by aligning the various stages of disease progression with susceptible phenology. The resulting information facilitates more targeted and timely interventions, enhancing the effectiveness of disease management in vineyards.<\/p>\n\n\n\n Furthermore, model based DSS can predict the duration of treatment efficacy and the likelihood of wash-off if rain occurs. This means that treatment renewal is no longer a matter of speculation but based on more empirical grounds. Excess applications are avoided because growers know the protection level of their plants and can therefore save money and decrease environmental impacts while maintaining vineyard health, grape quality, and ultimately increase their profitability.<\/p>\n\n\n\n Finally, DSS offer the benefit of forecasting imminent risks using weather. Planning treatments in advance is essential to guarantee sufficient protection, especially considering that phytosanitary products for fungal diseases primarily act preventively rather than curatively. Sometimes a few days of advance can make a huge difference on final disease levels and save the yield.<\/p>\n\n\n\n For example, in 2023, an unusually wet start to the growing season in southern and central Italy led to a devastating outbreak of downy mildew. The combination of frequent rain and warm temperatures in May and June created ideal conditions for fungal growth in regions where such diseases were historically rare, resulting in significant yield losses. <\/p>\n\n\n\n Many growers reacted slowly because they underestimated the risk, assuming their vineyards were historically less prone to disease. Consequently, they initiated treatments too late, unable to contain the infections in time.<\/p>\n\n\n\n In such situations, the integration of DSS, offering predictive insights into disease risk, proves invaluable for formulating effective phytosanitary strategies to protect yield. As climate-change driven disruptions become more frequent, growers will increasingly struggle to rely solely on past experiences to predict risks. Thus, the significance of predictive tools like those described will only grow more evident as time progresses.<\/p>\n\n\n\n![\"\"](\"https:\/\/vintel-itk.com\/wp-content\/uploads\/2024\/03\/Vulne-Pheno-eng--1024x437.jpg\")
Predicting plant-environment-pathogen interactions and treatment efficacy<\/strong><\/h2>\n\n\n\n
The key to a successful protection strategy: Anticipating risk<\/strong><\/h2>\n\n\n\n
The case of Italy in 2023: When experience is not enough<\/strong><\/h4>\n\n\n\n