Evaluating equations estimating change in swine feed intake during heat and cold stress1

White, Robin R.; Miller, Phillip S.; Hanigan, M.D.

doi:10.2527/jas.2015-9220

Cited by 16 publications

(10 citation statements)

References 51 publications

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“…The weighting factor was calculated by dividing the reciprocal SEM by the mean reciprocal SEM within analysis type. Dividing by the mean reciprocal SEM normalized the weighting factors to 1 irrespective of analysis type, effectively standardizing the weights across fixed effect or mixed models (White et al, 2015;Roman-Garcia et al, 2016). In studies that reported SEM as less than one-fourth the mean SEM, the SEM was set to one-fourth of the mean SEM across all studies to prevent over weighting (Firkins et al, 2001;Roman-Garcia et al, 2016;White et al, 2016).…”

Section: Weighting Strategiesmentioning

confidence: 99%

Meta-analysis of endophyte-infected tall fescue effects on cattle growth rates

Liebe

White

2018

Journal of Animal Science

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The objective of this study was to quantitatively summarize literature reporting endophyte-infected (Neotyphodium coenophialum) tall fescue (Festuca arundinacea) effects on cattle ADG. This meta-analysis evaluated endophyte infection level, climate, and forage yield using a literature dataset of 138 treatments from 20 articles. Three infection level measurements were tested: endophyte infection as a percentage of infected tillers (E%); ergovaline concentration in ppb ([E]); and total ergot alkaloid concentration ([TEA]). Three types of climate variables were used: base values (temperature, humidity, and relative humidity), climate indices (heat index and temperature-heat index [THI]), and novel climate variables accounting for duration of climate effects. Mixed effect models, weighted by 1/SEM, including a random effect of study were built for each factorial combination of measurement method and climate variable group. Because many studies were missing SEM, two datasets were used: one containing only data with SEM reported and one that also included missing-SEM data. For the complete-SEM dataset (CSD), models were weighted by 1/SEM. In the missing-SEM dataset (MSD) the mean reported 1/SEM was assigned as the weight for all missing SEM treatments. Although 18 initial models were created (2 × 3 × 3 factorial approach), the backward stepwise derivation resulted in models that included only endophyte infection level, suggesting a negative relationship between infection level and ADG. The CSD models predicted ADG to decrease 39 and 33 g/d with each increase of 100 ppb of [TEA] and [E], and by 39 g/d for each increase of 10% E%. In the MSD dataset, predicted ADG decreased by 39 and 33 g/d with each increase of 100 ppb of [TEA] and [E], and by 47 g/d for each increase of 10% E%. All relationships reported had P < 0.05. After visual inspection of the data, piecewise regression was used to identify an infection threshold (IT) of 60 ppb [E] and 11 E%, where the effect of infection level was constant on either side of the IT. The ADG was 40% and 49% greater for infection levels below the IT for [E] and E%, respectively. Across THI values in the analysis, ADG decreases ranged from 11.2% to 45.0% for cattle grazing endophyte-infected tall fescue compared to non-ergot alkaloid endophyte infected tall fescue. Pasture E%, [E], and [TEA] have a negative relationship with ADG in growing cattle, and increasing temperature decreases ADG when infection level is greater than the IT.

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Section: Weighting Strategiesmentioning

confidence: 99%

Meta-analysis of endophyte-infected tall fescue effects on cattle growth rates

Liebe

White

2018

Journal of Animal Science

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“…The present study found that sows mated within 6 days after weaning had higher effects of heat stress on farrowing rate than the other sows. Under heat conditions, sows are not able to have enough feed intake during lactation to maintain their body condition (Koketsu, Dial, Pettigrew, Marsh, & King, ; White, Miller, & Hanigan, ), and thus their body condition at weaning could be poorer than usual. Sows mated after 7 days have a time to recover their condition from weaning to service, whereas sows mated within 6 days after weaning did not have enough time to recover their condition.…”

Section: Discussionmentioning

confidence: 99%

Quantitative assessment of the effects of outside temperature on farrowing rate in gilts and sows by using a multivariate logistic regression model

Sasaki

Fujie

Nakatake

et al. 2018

Animal Science Journal

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It is well known that pigs are sensitive to heat stress, but few studies have assessed the critical temperature that affects farrowing rate. Therefore, the objective of the present study was to assess the effects of outside temperature on farrowing rate by using a multivariate logistic regression model. Data were obtained from 25 commercial farms, including 26,128 service records for gilts and 120,655 service records for sows. Two variables, maximum temperature (MAX) and temperature humidity index (THI), were used as an indicator for climate conditions. In gilts, an interaction between climate conditions and service number was associated with farrowing rate (p < .05). In the first service, farrowing rate decreased as climate conditions increased, whereas no relationship was found in the second service or later. In sows, farrowing rate at first service decreased as MAX increased from 22°C or THI increased from 66 (p < .05), but no apparent reduction under heat conditions was found in the second service or later. Additionally, effect of heat stress on farrowing rate in parities 1-2 was higher than those in parities 3-5 and ≥6 (p < .05). These results can be applied to field conditions as a standard for the critical temperature for farrowing rate.

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“…A quadratic response to temperature was employed because animals likely have some physical maximum intake due to digestive tract space limitations and at extremely high temperatures, animals stop eating altogether. An initial comparison of linear and quadratic temperature models suggested lower RMSPE and higher CCC for the quadratic fit (White et al in press). The residuals of the quadratic model were assessed against additional explanatory variables available within the data set.…”

Section: Methodsmentioning

confidence: 99%

Modelling cross-species feed intake responses to thermal stress

White

Hanigan

2015

J. Agric. Sci.

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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.

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Evaluating equations estimating change in swine feed intake during heat and cold stress1

Cited by 16 publications

References 51 publications

Meta-analysis of endophyte-infected tall fescue effects on cattle growth rates

Meta-analysis of endophyte-infected tall fescue effects on cattle growth rates

Quantitative assessment of the effects of outside temperature on farrowing rate in gilts and sows by using a multivariate logistic regression model

Modelling cross-species feed intake responses to thermal stress

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