Recent advances in our understanding of methanogenesis have led to the development of antimethanogenic feed additives (AMFA) that can reduce enteric methane (CH₄) emissions to varying extents, via direct targeting of methanogens, alternative electron acceptors, or altering the rumen environment. Here we examine current and new approaches used for the accounting (i.e., quantification) of enteric CH₄ abatement by the use of AMFA in the livestock sector from the individual animal to the global scale. Along with this process, recommendations are provided on how to account for the mitigation potential at the animal level, as well as in farm-scale models, emissions trading schemes, life cycle assessment, and carbon (C) footprinting tools, and in regional and national inventories. In addition, an assessment of uncertainties and potential trade-offs and off-setting with the use of AMFA (i.e., efficacy vs. effectiveness, upstream and downstream emissions) is provided. The accounting of on-farm enteric CH₄ emissions and benefits from the use of AMFA starts with the ruminant animal (with estimates obtained from a range of approaches, from simple empirical emission factors or equations to complex process-based models) and goes all the way to national and supranational accounting. The choice of methodologies and levels of complexity to account for mitigation of enteric CH₄ (or total GHG) emissions in livestock systems must be tailored to the scale of analysis aimed, the availability of input data to represent contextualized conditions, and the accounting objectives (e.g., academic exercise vs. producer's GHG certification vs. national GHG inventory). The accounting of enteric CH₄ mitigating effects needs to consider the AMFA delivery methods and synergies and trade-offs of GHG emissions at levels before and beyond (upstream and downstream) the animal to fully assess the impact of AMFA use. At large, the accounting of methane abatement by feed additives remains to be fully assessed beyond experimental results (efficacy) to address pragmatism (effectiveness), potential for adoption, and societal acceptance.

Brody, Gompertz, Logistic, Richards and von Bertalanffy functions were compared as descriptors of growth in 319 coypus (Myocastor coypus) of Greenland and Silver varieties, from birth to 10–19 months of age. Several criteria were considered for the selection of the model: (a) bias in the asymptotic weight; (b) bias in birth weight; (c) error sum of squares; (d) error mean squares weighted by the number of animals converging in all functions; (e) determination coefficient. To adjust the curves NLIN procedure from SAS and iterative method of Marquardt were applied. A mixed model was used to analyse the parameters of the selected growth curve (asymptotic weight, A; constant of integration, B; and maturation rate, k) which included effects of farm, variety, sex, sire within farm and variety, reproductive status within sex, birth season and first order interactions. The effect of sire was considered as random. The von Bertalanffy function was selected and mean values for the parameters of this function were 8016 g and 0·008 g/g per day, for A and k respectively. There were differences in the estimated parameters between sexes; males had larger values of A and k than females.Weights at fixed ages (birth, weaning, 6–7 months) were analysed, as well as the final recorded weight and average postweaning gain. The model of analysis was similar to the previous one. There were no differences between varieties. Significant differences between sexes for almost all studied traits were detected. With age, weight difference between males and females increased. At 6 months of age, males were 37% heavier than females. Male average daily gain was higher than that of females, sexual dimorphism being evident in all analysed traits.

El objetivo de este estudio de simulación fue evaluar la respuesta productiva, económica y de emisión de gases de efecto invernadero (GEI) en establecimientos típicos de la Pampa Deprimida de Argentina. Se utilizó información regional y un modelo de simulación dinámico, bioeconómico y ambiental (SIMUGAN) para evaluar dos tipos de sistemas contrastantes de los 4 identificados en un trabajo previo. Estos dos sistemas se tomaron como línea de base: Productor tradicional de escala media (Tr) y Empresa local tecnificada de escala grande (Te). Ambos escenarios fueron intensificados con la inclusión de festuca bajo buenas prácticas de manejo (5 o 10% de la superficie del establecimiento) y el adelanto de la edad al primer servicio de las vaquillonas (27 a 15 meses) para los planteos con 10% de la superficie con festuca. Las intensidades de emisión (IE) de GEI por unidad de producto) variaron según el nivel de intensificación de forma similar para Tr y Te (entre 16,4 y 19,9 kg CO₂ eq kg PV^-1). El efecto aditivo de la incorporación de festuca a un 10% de la superficie con el adelanto de la edad al primer servicio de las vaquillonas permitió reducir la IE hasta un 17%. Los resultados sugieren que es posible adoptar tecnologías disponibles para alcanzar los objetivos de reducción de IE. Aunque se requiere información experimental complementaria, este estudio provee información de utilidad referida a GEI para los tomadores de decisiones, políticos y productores, de modo de favorecer mayor promoción/adopción de tecnologías al tener en cuenta su respuesta económica.

Background and Aim: Nitrate (NO3–) reduces enteric methane emissions and could be a source of non-protein nitrogen in ruminant feeds. Nonetheless, it has a potential toxic effect that could compromise animal health and production. The purpose of this study was to determine the effects of progressive inclusion of NO3– in the diet on the hematological, biochemical, and blood gases parameters, in turn, the effects on feed intake and live weight gain (LWG) in Holstein calves. Materials and Methods: Eighteen Holstein heifers and steers (nine animals/treatment) were maintained in individual pens for 45 days. Animals were randomly allocated to either a control or nitrate diet (ND) (containing 15 g of NO3–/kg of dry matter [DM]). The biochemical parameters and blood gases were analyzed only in the NO3– group on days: -1, 1, 7, 13, 19, and 25 corresponding to 0, 20, 40, 60, 80, and 100% of the total inclusion of NO3– in the diet, respectively. In addition, DM intake (DMI) and LWG were evaluated among dietary treatments. Results: Feeding the ND did not influence DMI or LWG (p>0.05). Methemoglobin (MetHb) and deoxyhemoglobin increased according to the NO3– concentrations in the diet (p<0.05), while an opposite effect was observed for oxyhemoglobin and carboxyhemoglobin (p<0.05). Hematocrit levels decreased (p0.05). However, glucose, urea, aspartate aminotransferase (AST), and retinol concentrations increased (p<0.05) according to the NO3– concentrations in the diet. Conclusion: This study confirmed that the progressive inclusion of 123 g of NO3–/animal/day in the diet could be safe without affecting DMI and LWG of Holstein calves. In turn, a dose-response effect of the MetHb, glucose, urea, AST, and retinol was observed, but these values did not exceed reference values. These results highlighted the importance of using a scheme of progressive inclusion of NO3– in the diet of calves to reduce the risks of NO3– toxicity.

Soil organic carbon (SOC) stocks play an important role in ecosystem functioning and climate regulation. These stocks are declining in many tropical dry forests due to land-use change and degradation. Data on topsoil (0–300 mm) organic C stocks from six experiments conducted in the Dry Chaco region, the world’s largest dry tropical forest, were used to test the predictive performance of the Rothamsted Carbon Model (RothC) after its implementation in an object-oriented graphical programming language. RothC provided promising predictions (i.e., precise and accurate) of the SOC stocks under two representative land covers in the region, native forest and Rhodes grass [relative prediction error (RPE) < 10%, concordance correlation coefficient (CCC) > 0.9, modelling efficiency (MEF) > 0.7]. Comparatively, model predictions of the SOC stocks under degraded Rhodes grass swards were suboptimal. The predictions were sensitive to C inputs; under native forests and Rhodes grass, a high C input improved the predictive performance of the model by reducing the mean bias and increasing the MEF values, compared with mean and low C inputs. Larger datasets and revisiting some of the underlying assumptions in the SOC modelling will be required to improve the model’s performance, particularly under the degraded Rhodes grass land cover.