Data mining to estimate broiler mortality when exposed to heat wave

Scientia Agricola

Marcos Martinez Vale²  Daniella Jorge De Moura¹  Irenilza De Alencar Nääs²  Stanley Robson De Medeiros Oliveira¹  Luiz Henrique Antunes Rodrigues² 

[1] ,UNICAMP FEAGRI Campinas SP ,Brasil

关键词: THI;    broiler production;    environmental data;    ITU;    frango de corte;    dados ambientais;   

DOI  :  10.1590/S0103-90162008000300001

来源: SciELO

【 摘 要 】

Heat waves usually result in losses of animal production since they are exposed to thermal stress inducing an increase in mortality and consequent economical losses. Animal science and meteorological databases from the last years contain enough data in the poultry production business to allow the modeling of mortality losses due to heat wave incidence. This research analyzes a database of broiler production associated to climatic data, using data mining techniques such as attribute selection and data classification (decision tree) to model the impact of heat wave incidence on broiler mortality. The temperature and humidity index (THI) was used for screening environmental data. The data mining techniques allowed the development of three comprehensible models for estimating specifically high mortality during broiler production. Two models yielded a classification accuracy of 89.3% by using Principal Component Analysis (PCA) and Wrapper feature selection approaches. Both models obtained a class precision of 0.83 for classifying high mortality. When the feature selection was made by the domain experts, the model accuracy reached 85.7%, while the class precision of high mortality was 0.76. Meteorological data and the calculated THI from meteorological stations were helpful to select the range of harmful environmental conditions for broilers 29 and 42 days old. The data mining techniques were useful for building animal production models.

【 授权许可】

CC BY   
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