Identification of errors and factors associated with errors in data from electronic swine feeders1

Abstract

Electronic swine feeders are used to automatically measure individual feed intake on group-housed pigs, but the resulting data contain errors caused by feeder malfunctions and animal-feeder interactions. The objectives of this study were to 1) develop criteria to identify errors in data from an electronic feeder that is predominant in the United States; 2) evaluate the frequency of errors in data from three consecutive experiments using the same feeders; and 3) identify factors associated with errors. Across experiments, data included 1,878,321 feed intake records (visits) on 1,721 pigs and 124 pens. Sixteen criteria were developed to detect errors in seven variables related to feed trough weights and times. Logistic regression was used to identify factors associated with the presence or absence of each error type in identified visits (visits where the feeder recognized a transponder) using a model that included the fixed effects of replicate, sex, linear and quadratic effects of day on test, and random effects of feeder within replicate, pig within feeder within replicate, test day within replicate, and week within feeder within replicate. Frequencies of error types in identified visits varied considerably within and between experiments. Errors in feed trough weights were more frequent than errors in time. Percentage of identified visits and of daily feed intake records with at least one error ranged from 4.3 to 18.7% and from 17.2 to 50.0%, respectively, and decreased from the first to the last experiment, reflecting the increasing ability of the managers to operate the feeders. Replicate, sex, test day, feeder within replicate, pig, and day within replicate affected the number of errors that occurred, but their effect varied among error types. Week-to-week variation within a feeder and replicate had the largest effect on number of errors, which was likely associated with feeder management. Results indicate that the frequency of errors in data from electronic swine feeders is substantial, but visits with errors can be identified and their frequency can be decreased by proper feeder management.

Accounting for variability among individual pigs in deterministic growth models

“…Among these pig-days, 4% were manually attributed to individual pigs and 17% were attributed equally to all penmates. Identification errors explained the majority of this attribution (17% of the records), which is consistent with values of Casey et al (2005;4% to 19% across three successive experiments). For each pig, the average DFI (ADFI) was calculated as the sum of individual DFI during the whole experiment divided by the duration of the experiment.…”

“…The DFI was measured individually through an automatic feed dispenser (Acema 64, Acemo, Pontivy, France), as described by Labroue et al (1994). Two potential problems can occur with electronic feeders: feed disappearance and identification of pigs (Casey et al, 2005). Each week, total feed disappearance from the dispenser was measured manually and compared with the sum of individual DFI records.…”

Feeder Use Patterns in Group-Housed Pregnant Sows Fed With an Unprotected Electronic Sow Feeder (Fitmix)

“…Therefore, a threshold was set to classify as errors in those visits with duration longer than expected. Eor the duration of the visits, a histogram identified the point in the tail of the distribution at which the frequency showed a substantial decrease (Casey et al, 2005).…”

Feed efficiency effects on barrow and gilt behavioral reactivity to novel stimuli tests

“…Feed intake data recorded using the FIRE ® feeders were edited following procedures outlined by Casey et al (2005). Average daily feed intake was calculated as described by Cai et al (2008).…”