Factors associated with market weight on three New Zealand pig farms
An observational study to identify performance parameters that allow predicting market weight on commercial pig farms
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The aim of this observational study was to identify performance parameters, which can be used to predict market weight of a batch of pigs on commercial farms. Weekly retro- and prospective production records were obtained from three New Zealand pig farms. Observation periods on farms A, B, and C were 140, 127 and 90 weeks. Two modelling approaches were used for multivariable analysis to account for autocorrelation: An autoregressive (AR) and an ordinary least squares (OLS) regression model ( naive approach ). Each farm was analysed separately. Using an AR-model, four production parameters (wea...
The aim of this observational study was to identify performance parameters, which can be used to predict market weight of a batch of pigs on commercial farms. Weekly retro- and prospective production records were obtained from three New Zealand pig farms. Observation periods on farms A, B, and C were 140, 127 and 90 weeks. Two modelling approaches were used for multivariable analysis to account for autocorrelation: An autoregressive (AR) and an ordinary least squares (OLS) regression model ( naive approach ). Each farm was analysed separately. Using an AR-model, four production parameters (weaning age, two sample weights and days to market) were identified across all farms that were effective in predicting market weight with accuracies greater than 70%. All AR-models yielded stationary and normally distributed residuals. In contrast, residuals of the OLS-models showed remaining autocorrelation on two farms indicating biased model estimates. Using an AR-model also has the advantage that immediate future observations can be forecasted. This is particularly useful as predictor variables (apart from 'Days to market') could be obtained a month prior to marketing.
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