Modeling the trade-off between diet costs and methane emissions: A goal programming approach

AbstractEnteric methane emission is a major greenhouse gas from livestock production systems worldwide. Dietary manipulation may be an effective emission-reduction tool; however, the associated costs may preclude its use as a mitigation strategy. Several studies have identified dietary manipulation strategies for the mitigation of emissions, but studies examining the costs of reducing methane by manipulating diets are scarce. Furthermore, the trade-off between increase in dietary costs and reduction in methane emissions has only been determined for a limited number of production scenarios. The objective of this study was to develop an optimization framework for the joint minimization of dietary costs and methane emissions based on the identification of a set of feasible solutions for various levels of trade-off between emissions and costs. Such a set of solutions was created by the specification of a systematic grid of goal programming weights, enabling the decision maker to choose the solution that achieves the desired trade-off level. Moreover, the model enables the calculation of emission-mitigation costs imputing a trading value for methane emissions. Emission imputed costs can be used in emission-unit trading schemes, such as cap-and-trade policy designs. An application of the model using data from lactating cows from dairies in the California Central Valley is presented to illustrate the use of model-generated results in the identification of optimal diets when reducing emissions. The optimization framework is flexible and can be adapted to jointly minimize diet costs and other potential environmental impacts (e.g., nitrogen excretion). It is also flexible so that dietary costs, feed nutrient composition, and animal nutrient requirements can be altered to accommodate various production systems

Challenges of the transition to a low carbon, more electric future:From here to 2050

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Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Inhibited methanogenesis in the rumen of cattle: microbial metabolism in response to supplemental 3-nitrooxypropanol and nitrate

3-Nitrooxypropanol (3-NOP) supplementation to cattle diets mitigates enteric CH emissions and may also be economically beneficial at farm level. However, the wider rumen metabolic response to methanogenic inhibition by 3-NOP and the (Formula presented.) intermediary metabolite requires further exploration. Furthermore, (Formula presented.) supplementation potently decreases CH emissions from cattle. The reduction of (Formula presented.) utilizes H and yields (Formula presented.), the latter of which may also inhibit rumen methanogens, although a different mode of action than for 3-NOP and its (Formula presented.) derivative was hypothesized. Our objective was to explore potential responses of the fermentative and methanogenic metabolism in the rumen to 3-NOP, (Formula presented.) and their metabolic derivatives using a dynamic mechanistic modeling approach. An extant mechanistic rumen fermentation model with state variables for carbohydrate substrates, bacteria and protozoa, gaseous and dissolved fermentation end products and methanogens was extended with a state variable of either 3-NOP or (Formula presented.). Both new models were further extended with a (Formula presented.) state variable, with (Formula presented.) exerting methanogenic inhibition, although the modes of action of 3-NOP-derived and (Formula presented.) -derived (Formula presented.) are different. Feed composition and intake rate (twice daily feeding regime), and supplement inclusion were used as model inputs. Model parameters were estimated to experimental data collected from the literature. The extended 3-NOP and (Formula presented.) models both predicted a marked peak in H emission shortly after feeding, the magnitude of which increased with higher doses of supplement inclusion. The H emission rate appeared positively related to decreased acetate proportions and increased propionate and butyrate proportions. A decreased CH emission rate was associated with 3-NOP and (Formula presented.) supplementation. Omission of the (Formula presented.) state variable from the 3-NOP model did not change the overall dynamics of H and CH emission and other metabolites. However, omitting the (Formula presented.) state variable from the (Formula presented.) model did substantially change the dynamics of H and CH emissions indicated by a decrease in both H and CH emission after feeding. Simulations do not point to a strong relationship between methanogenic inhibition and the rate of (Formula presented.) and (Formula presented.) formation upon 3-NOP supplementation, whereas the metabolic response to (Formula presented.) supplementation may largely depend on methanogenic inhibition by (Formula presented.).The research was funded by DSM Nutritional Products (Basel, Switzerland)

A structural equation model to analyze energy utilization in lactating dairy cows

Energy balance trials from lactating cows have traditionally been analyzed using the regression approach. However, univariate analysis of mutually interacting animal traits can provide biased parameter estimates if one trait is used as an explanatory variable to model a second trait and the sub-model of the first trait is ignored (Gianola and Sorensen, 2004). Moreover, multivariate methods should be preferred because animal responses are correlated and relationships among responses can be quantified through the use of structural equations. Furthermore, the US current energy evaluation system of dairy cattle (NRC, 2001) relies on energetic parameters from the 1960’s but over the past decades genetic improvement of dairy cattle has substantially increased the ability of cows to produce milk. The objective of the present study was to analyze energy utilization in lactating cows using structural equations

Bayesian analysis of energy balance data from growing cattle using parametric and non-parametric modelling

Linear and non-linear models have been extensively utilised for the estimation of net and metabolisable energy requirements and for the estimation of the efficiencies of utilising dietary energy for maintenance and tissue gain. In growing animals, biological principles imply that energy retention rate is non-linearly related to the energy intake level because successive increments in energy intake above maintenance result in diminishing returns for tissue energy accretion. Heat production in growing cattle has been traditionally described by logarithmic regression and exponential models. The objective of the present study was to develop Bayesian models of energy retention and heat production in growing cattle using parametric and non-parametric techniques. Parametric models were used to represent models traditionally employed to describe energy use in growing steers and heifers whereas the non-parametric approach was introduced to describe energy utilisation while accounting for non-linearities without specifying a particular functional form. The Bayesian framework was used to incorporate prior knowledge of bioenergetics on tissue retention and heat production and to estimate net and metabolisable energy requirements (NEM and MEM, respectively), and the partial efficiencies of utilising dietary metabolisable energy for maintenance (km) and tissue energy gain (kg). The database used for the study consisted of 719 records of indirect calorimetry on steers and non-pregnant, non-lactating heifers. The NEM was substantially larger in energy retention models (ranged from 0.40 to 0.50 MJ/kg BW0.75.day) than were NEM estimates from heat-production models (ranged from 0.29 to 0.49 MJ/kg BW0.75.day). Similarly, km was also larger in energy retention models than in heat production models. These differences are explained by the nature of y-intercepts (NEM) in these two models. Energy retention models estimate fasting catabolism as the y-intercept, while heat production models estimate fasting heat production. Conversely, MEM was virtually identical in all models and approximately equal to 0.53 MJ/kg BW0.75.day in this database

Prediction of nitrogen use in dairy cattle: a multivariate Bayesian approach

Quantification of dairy cattle nitrogen (N) excretion and secretion is necessary to improve the efficiency with which feed N is converted to milk N (ENU). Faecal and urinary N excretion and milk N secretion are correlated with each other and thus are more accurately described by a multivariate model that can accommodate the covariance between the three observations than by three separate univariate models. Further, by simultaneously predicting the three routes of excretion and taking advantage of the mass balance relationships between them, covariate effects on N partitioning from feed to faeces and absorbed N and from absorbed N to milk and urine N and animal ENU can be estimated. A database containing 1094 lactating dairy cow observations collated from indirect calorimetry experiments was used for model development. Dietary metabolisable energy content (ME, MJ/kg DM) increased ENU at a decreasing rate, increased the efficiency with which feed N was converted to absorbed N and decreased the efficiency with which absorbed N was converted to milk N. However, the parameter estimate of the effect of ME on post-absorption efficiency was not different from zero when the model was fitted to a data subset in which net energy and metabolisable protein were at or above requirement. This suggests the effect of ME on post-absorption N use is dependent on the energy status of the animal