The optimization of the production of the vineyards of the D. O. Ribeiro. This was the objective of a doctoral thesis of a research of the University of Vigo, carried out by Estefanía González. In his study he made several prediction models for disease control in vine cultivation. It was based on plant-pathogen-environment interactions. In the same way it focused on studying the factors that influence the final production of the grape. In 98% of the cases analyzed, it was possible to anticipate the variation in grape production data. This is two months in advance of the harvest.
The study that includes the research was executed in a vineyard in the town of Cenlle in Ourense. It was developed between 2016 and 2019. The grape varieties studied were treixadura, godello, loureira and albariño. Phenological observations were carried out in 22 plants of each variety. Likewise, studies of pollen production and plant fertility were carried out. Ten plants selected from each variety were used. In addition, the collection of meteorological data from the MeteoGalicia station in Leiro was carried out.
"The bioclimatic indicators developed in this research were based on the identification and evaluation of the main periods of risk of infection. They were performed based on phenological, meteorological and atmospheric spore levels. They were also used to reduce the number of phytosanitary treatments in the vineyard. In the same way, we tried to effectively improve the management of crop diseases. For this it relied on integrated pest control strategies." This is how the researcher explains it.
As a result of this fieldwork, the thesis proposes a methodology and prediction models. The research explains "A high proportion of the variability of the grape production data was carried out. These results are also offered approximately two months in advance. It allows the organization and optimization of the tasks of management of the work of harvest, harvest and winery". In addition, the researcher highlights that the proposed model is a valuable resource. It allows to consider the variation in crop productivity associated with the impacts of climate change. It takes into account the main meteorological factors and morphological and floral variables that affect the final harvest.
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