Modelling cross-species feed intake responses to thermal stress

Abstract

SUMMARYThe objectives of the current study were to compare and model feed intake responses to ambient temperature across species and to assess opportunities to use cross-species (CS) data to parameterize models when species-specific (SS) data were limited. Literature searches were conducted to identify studies reporting intake during thermal stress compared with thermoneutral (TN) conditions. The resulting data set comprised 614 treatment means from 108 studies on livestock responses to thermal stress. An analysis of variance was conducted with the CS data set to identify the effects of species, temperature and species by temperature interactions on intake as (fractional feed intake; FFI). Four models were derived from the CS data set and root mean squared prediction error (RMSPE) and concordance correlation coefficients (CCC) of these models were compared with models of the same form derived from SS data sets. Models used explanatory variables for (1) duration of exposure; (2) mean temperature; (3) minimum and maximum temperatures; or (4) difference between minimum and maximum temperatures. An additional model accounting for temperature and stage of production was derived from the SS data. Analysis of variance demonstrated that the species by temperature interaction did not have a significant effect on FFI. Across species, intake decreased with temperature. Notably, all species demonstrated a constant decrease in intake across the TN zone indicating the previous assumption of constant intake during thermoneutrality may be not fully valid. When compared on a SS basis, SS-derived models had marginally lower RMSPE and higher CCC when compared with models derived from the CS data sets. The model fit with production data had the lowest RMSPE and highest CCC within the study. When compared over temperature ranges with minimal data available in some species (e.g., cold stress), using CS models often resulted in decreased RMSPE and improved CCC when compared with SS models. Although fitting models based on SS data allows for incorporating unique covariates, like level of production, fitting responses based on CS data can help to improve model estimates when knowledge gaps exist.