Abstract
The aim of this study is the application of advanced modeling techniques to identify powdery mildew tolerant cultivars and reduce fungicides and energy consumption. The energy savings resulting from the increased efficiency of the use of fungicides is an innovative aspect investigated within the project AGROENER researching on energy efficiency. In this preliminary study, investigations through phenotyping methods could represent a potential solution, especially if they are used in combination with tools and algorithms able to extract and convert a large amount of data. Twelve different grapevine cultivars were tested. The construction of an artificial model, characterized by absolute optima of response to a pathogen (i.e., low values of disease incidence and severity and first day of the pathogen appearance), allowed us to cover the potential variability of a real dataset. To identify the cultivars that tolerate powdery mildew the most, two Soft Independent Modeling of Class Analogy (SIMCA) models were built. The modeling efficiencies, indicated by sensitivity value, were equal to 100%. These statistical multivariate classifications identified some of these tolerant cultivars, as the best responding to the pathogen.
Highlights
IntroductionThe most common grapevine (Vitis sp.) diseases have significantly increased in Europe
In the last years, the most common grapevine (Vitis sp.) diseases have significantly increased in Europe
The aim of this study is the application of advanced modeling techniques to identify powdery mildew tolerant cultivars and reduce fungicides and energy consumption
Summary
The most common grapevine (Vitis sp.) diseases have significantly increased in Europe. Solutions leading to an increased sustainability in viticulture are highly desirable, it is crucial to adopt environmentally friendly methods able to reduce fungicides use and energy consumption In this context, the development and implementation of high-throughput phenotyping methods represents a key tool to acquire large quantity of data under controlled environments [3,4]. Its primary infection has the need of at least 12–15 h of continuous wetness at 10–15 ◦C To limit these problems, the crop responses to the disease should be investigated following both specific times and spatial scheduling according to the disease onset, growth stage and pathogen development [7,8]. The fungicide treatments increase production costs while negatively affecting the environment, product quality and safety
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