Published
2020-07-07
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Analysis of productive and socio-business variables of avocado hass producers in two municipalities of Cauca for the creation of key performance indicators (KPI) and the improvement of decision-making in the rural sector

DOI: https://doi.org/10.22490/21456453.3159
Section
Área Agrícola
Héctor Fabio Lopez Castaño Universidad Nacional Abierta y a Distancia
Jéssica Almeida Braga Universidad Nacional Abierta y Distancias
Max Brandão de Oliveira Universidade Federal do Piauí/Brasil

Contextualization: The article addresses the application of multivariate data analysis techniques in the agricultural sector, in order to characterize two groups of hass avocado farmers, based on data collected by entities that traditionally provide rural technical assistance in the country, the municipal technical assistance units (UMATAS), as well as facilitate data collection in the field and shedding light on the creation of new performance indicators

Knowledge gap: Poor application of data analysis methods and instruments in the rural technical assistance process, which allow defining recommendations for activities, inputs and labor more specific to the reality of the different associations or individuals of agricultural producers

Objectives: 1) Analyze the data collected through the diagnostic instrument to the farms, RUAT, which is used by the Umatas to fulfill their role of rural technical support; 2) Apply the statistical techniques of classification and organization of data most pertinent to said characterization instrument; and 3) Determine the importance of the variables included in the diagnostic instruments for farms for the construction of indicators.

Methodology: From the review of several theoretical references on the steps necessary for the creation and selection of indicators, the following methodological steps were followed: Development of a conceptual framework, selection of variables, normalization of data, multivariate analysis and weighting of information; Other subsequent steps such as information aggregation, robustness and sensitivity analysis, may be part of subsequent work.

Results and conclusions: It was found that the variables that had greater weight in the characterization and grouping of producers were those belonging to the group of good agricultural practices and to that of production, demonstrating that with only 8 variables it is possible to have a good approximation to the characterization of the producers, instead of the more than 36 variables that make up the diagnostic instrument called the Registry of Users of Technical Assistance (RUAT)