Detection of fiber-digesting bacteria in the forestomach contents of llamas (Lama glama) by PCR

AbstractThe high fibrolytic activity and large biomass of strictly-anaerobic bacteria that inhabit the rumen makes them primarily responsible for the degradation of the forage consumed by ruminants. Llamas feed mainly on low quality fibrous roughages that are digested by an active and diverse microflora. The products of this fermentation are volatile fatty acids and microbial biomass, which will be used by the animals. The aim of this study was to detect the three major fiber-digesting anaerobic bacteria in the forestomach contents of llamas by PCR. In this study, we detected Ruminococcus albus, Ruminococcus flavefaciens and Fibrobacter succinogenes in the forestomach contents of eight native llamas from Argentina

LAMP technology: Rapid identification of Brucella and Mycobacterium avium subsp. paratuberculosis

In this study, we developed new sets of primers to detect Brucella spp. and M. avium subsp. paratuberculosis (MAP) through isothermal amplification. We selected a previously well-characterized target gene, bscp31, specific for Brucella spp. and IS900 for MAP. The limits of detection using the loop-mediated isothermal amplification (LAMP) protocols described herein were similar to those of conventional PCR targeting the same sequences. Hydroxynaphtol blue and SYBR GreenTM allowed direct naked-eye detection with identical sensitivity as agarose gel electrophoresis. We included the LAMP-based protocol in a rapid identification scheme of the respective pathogens, and all tested isolates were correctly identified within 2 to 3 h. In addition, both protocols were suitable for specifically identifying the respective pathogens; in the case of Brucella, it also allowed the identification of all the biovars tested. We conclude that LAMP is a suitable rapid molecular typing tool that could help to shorten the time required to identify insidious bacteria in low-complexity laboratories, mainly in developing countries

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