Abstract
Frequent BW monitoring of growing pigs can be useful for identifying production (e.g. feeding), health and welfare problems. However, in order to construct a tool which will properly recognize abnormalities in pigs’ growth a precise description of the growth process should be used. In this study we proposed a new model of pig growth accounting for daily fluctuations in BW. Body weight measurements of 1710 pigs (865 gilts and 843 barrows) originating from five consecutive batches from a Danish commercial farm were collected. Pigs were inserted into a large pen (maximum capacity=400) between November 2014 and September 2015. On average, each pig was observed for 42 days and weighed 3.6 times a day when passing from the resting to feeding area. Altogether, 243,160 BW measurements were recorded. A multilevel model of pig growth was constructed and fitted to available data. The BW of pigs was modeled as a quadratic function of time. A diurnal pattern was incorporated into the model by a cosine wave with known length (24 h). The model included pig effect which was defined as a random autoregressive process with exponential correlation. Variance of within-pigs error was assumed to increase with time. Because only five batches were observed, it was not possible to obtain the random effect for batch. However, in order to account for the batch effect the model included interactions between batch and fixed parameters: intercept, time, square value of time and cosine wave. The gender effect was not significant and was removed from the final model. For all batches, morning and afternoon peaks in the frequency of visits to the feeding area could be distinguished. According to results, pigs were lighter in the morning and heavier in the evening (minimum BW was reached around 1000 h and maximum around 2200 h). However, the exact time of obtaining maximum and minimum BW during the day differed between batches. Pigs had access to natural light and, therefore, existing differences could be explained by varying daylight level during observations periods. Because the diurnal amplitude for pig growth varied between batches from 0.9 to 1.4 kg, BW monitoring tools based on frequent measurements should account for diurnal variation in BW of pigs. This proposed description of growth will be built into a monitoring tool (a dynamic linear model) and applied to farm data in future studies.
Highlights
IntroductionFor pigs observed during short time intervals, growth has been defined by linear functions with the intercept representing initial BW and the slope expressing an increase in BW (Toft et al, 2005)
It has been assumed that the growth of pigs is well described
The lowest initial BW of pigs was identified in Batch 2 (51 kg) while the highest was identified in Batch 3 (60.5 kg)
Summary
For pigs observed during short time intervals, growth has been defined by linear functions with the intercept representing initial BW and the slope expressing an increase in BW (Toft et al, 2005). For pigs observed over longer time intervals, periods with faster and slower growth rates have been expressed in the form of Gompertz functions (Niemi et al, 2015). Such description of growth is adequate for traditional BW monitoring, which involves, for example, moving pigs from pens to a weighing crate on a daily, weekly or monthly bases. For automatic growth monitoring to be useful, a more precise description of growth might be necessary
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