Qualitative Dataset CSV

Association Rules Mining (ARM) is a widely used approach in data mining to discover trends or patterns of behavior present in databases. Therefore, quantitative and qualitative value databases become very important when used as resources to be analyzed to support decision-making.Nowadays, there are a large number of databases of quantitative value that express information from different fields of application, however, in the field of agriculture, there are few databases constructed that take into account the empirical knowledge of farmers and that indicate the different productive tasks that a coffee growers performs throughout the coffee growing process. This document presents a database of qualitative value based on the empirical information from coffee growers in the department of Cauca, Colombia. The main objective is to detail all those practices or tasks of qualitative value such as the management of the crop or productive task, the use of bioindicators, the use of ancestral knowledge, and other external conditions such as the incidence of climatic conditions that have a certain degree of direct influence on crops, allowing with this type of datasets, to determine the correlation between those variables and characteristics that facilitate the identification and protection of the unleashing of pests and diseases in coffee crops.<br>This dataset contains empirical information from coffee growers, where they indicate different practices and productive task carried out in the cultivation of coffee and that would facilitate the analysis for a subsequent protection of the crop or decision-making to avoid the unleashing of pest and diseases in coffee crops.