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Qualitative analysis of goat and sheep production data using self‐organizing maps
Author(s) -
Magdalena R.,
Fernández C.,
Martín J. D.,
Soria E.,
Martínez M.,
Navarro M. J.,
Mata C.
Publication year - 2009
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/j.1468-0394.2009.00477.x
Subject(s) - computer science , production (economics) , self organizing map , milk production , qualitative analysis , set (abstract data type) , livestock , data set , qualitative property , data mining , data science , qualitative research , artificial intelligence , machine learning , biology , cluster analysis , zoology , ecology , social science , sociology , economics , macroeconomics , programming language
The aim of this study was to analyse the relationship between different small ruminant livestock production systems with different levels of specialization. The analysis is carried out by using the self‐organizing map. This tool allows high‐dimensional input spaces to be mapped into much lower‐dimensional spaces, thus making it much more straightforward to understand any set of data. These representations enable the visual extraction of qualitative relationships among variables (visual data mining), converting the data to maps. The data used in this study were obtained from surveys completed by farmers who are principally dedicated to goat and sheep production. With the self‐organizing map we found a relationship between qualitative and quantitative variables showing that more specialized farms have greater milk incomes per goat, highlighting farms that have a greater number of animals, better facilities (including milking machines) or animals fed with elaborated diets. The use of self‐organizing maps for the analysis of this kind of data has proven to be highly valuable in extracting qualitative conclusions and in guiding improvements in farm performance.