Bem-estar dos cordeiros submetidos ao transporte rodoviário e avaliação das carcaças e carnes

RESUMO: Objetivou-se avaliar o grau de bem-estar dos cordeiros submetidos ao transporte rodoviário e suas carcaças e carnes. Para isto, fez-se a avaliação dos parâmetros comportamentais durante o transporte, dos parâmetros fisiológicos após o desembarque e antes do abate e a caracterização das carcaças e carnes dos cordeiros. Realizaram-se quatro transportes rodoviários com durações crescentes (1h45min, 3h52min, 7h30min e 10h30min), cada transporte continha vinte cordeiros. O peso corporal dos animais foi de 36,64±2,13 kg antes do transporte. Os cordeiros foram abatidos 15 horas após o desembarque. Os cordeiros deitaram por pouco tempo (mediana igual à zero a cada 20min) em jornadas menores que 3h52min. O número de eventos potencialmente traumáticos foi baixo (mediana próxima a zero, a cada 20min) para quaisquer durações dos transportes. As concentrações de adrenalina e cortisol, bem como os metabólitos que são controlados por eles, foram semelhantes entre os tratamentos. Contudo, a massa das carcaças diminuiu e as concentrações de creatina quinase aumentaram linearmente quando os transportes foram mais longos, o que podem revelar diminuição do bem-estar. A qualidade da carne de cordeiros não sofreu interferências da duração dos transportes

Improving the accuracy of beef cattle methane inventories in Latin America and Caribbean countries

On-farm methane (CH4) emissions need to be estimated accurately so that the mitigation effect of recommended practices can be accounted for. In the present study prediction equations for enteric CH4 have been developed in lieu of expensive animal measurement approaches. Our objectives were to: (1) compile a dataset from individual beef cattle data for the Latin America and Caribbean (LAC) region; (2) determine main predictors of CH4 emission variables; (3) develop and cross-validate prediction models according to dietary forage content (DFC); and (4) compare the predictive ability of these newly-developed models with extant equations reported in literature, including those currently used for CH4 inventories in LAC countries. After outlier's screening, 1100 beef cattle observations from 55 studies were kept in the final dataset (∼ 50 % of the original dataset). Mixed-effects models were fitted with a random effect of study. The whole dataset was split according to DFC into a subset for all-forage (DFC = 100 %), high-forage (94 % ≥ DFC ≥ 54 %), and low-forage (50 % ≥ DFC) diets. Feed intake and average daily gain (ADG) were the main predictors of CH4 emission (g d−1), whereas this was feeding level [dry matter intake (DMI) as % of body weight] for CH4 yield (g kg−1 DMI). The newly-developed models were more accurate than IPCC Tier 2 equations for all subsets. Simple and multiple regression models including ADG were accurate and a feasible option to predict CH4 emission when data on feed intake are not available. Methane yield was not well predicted by any extant equation in contrast to the newly-developed models. The present study delivered new models that may be alternatives for the IPCC Tier 2 equations to improve CH4 prediction for beef cattle in inventories of LAC countries based either on more or less readily available data

Improving the accuracy of beef cattle methane inventories in Latin America and Caribbean countries

On-farm methane (CH4) emissions need to be estimated accurately so that the mitigation effect of recommended practices can be accounted for. In the present study prediction equations for enteric CH4 have been developed in lieu of expensive animal measurement approaches. Our objectives were to: (1) compile a dataset from individual beef cattle data for the Latin America and Caribbean (LAC) region; (2) determine main predictors of CH4 emission variables; (3) develop and cross-validate prediction models according to dietary forage content (DFC); and (4) compare the predictive ability of these newly-developed models with extant equations reported in literature, including those currently used for CH4 inventories in LAC countries. After outlier's screening, 1100 beef cattle observations from 55 studies were kept in the final dataset (~ 50 % of the original dataset). Mixed-effects models were fitted with a random effect of study. The whole dataset was split according to DFC into a subset for all-forage (DFC = 100 %), high-forage (94 % ≥ DFC ≥ 54 %), and low-forage (50 % ≥ DFC) diets. Feed intake and average daily gain (ADG) were the main predictors of CH4 emission (g d−1), whereas this was feeding level [dry matter intake (DMI) as % of body weight] for CH4 yield (g kg−1 DMI). The newly-developed models were more accurate than IPCC Tier 2 equations for all subsets. Simple and multiple regression models including ADG were accurate and a feasible option to predict CH4 emission when data on feed intake are not available. Methane yield was not well predicted by any extant equation in contrast to the newly-developed models. The present study delivered new models that may be alternatives for the IPCC Tier 2 equations to improve CH4 prediction for beef cattle in inventories of LAC countries based either on more or less readily available data