Determination of factors affecting dairy cattle: a case study of Ardahan province using data mining algorithms
AUTOR(ES)
Karadas, Koksal
FONTE
R. Bras. Zootec.
DATA DE PUBLICAÇÃO
28/11/2019
RESUMO
ABSTRACT This study was conducted to compare predictive performances of different data-mining algorithms for determining factors influencing the average daily milk yield at dairy cattle enterprises of Ardahan province, located in the Eastern Anatolia region of Turkey. The algorithms employed in the present study were Classification and Regression Tree (CART), Chi-Square Automatic Interaction Detector (CHAID), Exhaustive Chi-Square Automatic Interaction Detector (Exhaustive CHAID), Multivariate Adaptive Regression Splines (MARS), and Multilayer Perceptron (MLP). The MARS algorithm outperformed the other algorithms in the study. Visual results of CART revealed that the culture-breed cows with a lactation length greater than 237.500 days had the highest milk yield (10.64 kg/day). Culture-breed cows calving earlier than the 4th month gave the highest yield of approximately 10 kg/day in the regression tree of CHAID. The Exhaustive CHAID results were almost the same as the structure of the CHAID. The use of MARS may provide an opportunity to detect factors affecting milk production (breed, feed supply, type of milking, mastitis control, cow year group, and lactation length) and their interactions. Moreover, the MARS algorithm may be useful in making an accurate decision about increasing milk yield per cow.
Documentos Relacionados
- GestÃo do conhecimento usando Data Mining: estudo de caso na UFLA
- Credit analysis using data mining: application in the case of a credit union
- Factors affecting feed efficiency in dairy goats
- Strong-association-rule mining for large-scale gene-expression data analysis: a case study on human SAGE data
- Evaluation of an inexpensive needle test for the diagnosis of phosphorus deficiency and management of phosphorus supplementation for cattle: A multiple case study