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This study investigated the effects of 67 species of macroalgae on methanogenesis and rumen fermentation in vitro. Specimens were analyzed for their effect on ruminal fermentation and microbial community profiles. Incubations were carried out in an automated gas production system for 24-h and macroalgae were tested at 2% (feed dry matter basis) inclusion rate. Methane yield was decreased 99% by Asparagopsis taxiformis (AT) when compared with the control. Colpomenia peregrina also decreased methane yield 14% compared with control; no other species influenced methane yield. Total gas production was decreased 14 and 10% by AT and Sargassum horneri compared with control, respectively. Total volatile fatty acid (VFA) concentration was decreased between 5 and 8% by 3 macroalgae, whereas AT reduced it by 10%. Molar proportion of acetate was decreased 9% by AT, along with an increase in propionate by 14%. Asparagopsis taxiformis also increased butyrate and valerate molar proportions by 7 and 24%, respectively, whereas 3 macroalgae species decreased molar proportion of butyrate 3 to 5%. Vertebrata lanosa increased ammonia concentration, whereas 3 other species decreased it. Inclusion of AT decreased relative abundance of Prevotella, Bacteroidales, Firmicutes and Methanobacteriaceae, whereas Clostridium, Anaerovibrio and Methanobrevibacter were increased. Specific gene activities for Methanosphaera stadtmane and Methanobrevibacter ruminantium were decreased by AT inclusion. In this in vitro study, Asparagopsis taxiformis was most effective in decreasing methane concentration and yield, but also decreased total gas production and VFA concentration which indicates overall inhibition of ruminal fermentation. No other macroalgae were identified as potential mitigants of enteric methane., Scientific Reports, 13, ISSN:2045-2322

There is a growing call (and agreement) to transform food systems towards sustainable, just and healthy systems, understanding that transformation is about fundamental, system- wide changes, beyond piecemeal interventions (see Box 1 for the new FACCE-JPI approach to food systems). But, what does transformation look like? Is there a consensus about what we want to transform in food systems, who will do it and how? The objective of this policy brief is to call for fairer, more inclusive and eventually, more effective decisions on food systems transformations. For doing so, it focuses on decision-making under uncertainty, highlighting complexity and framings as two components of this: On one hand complexity requires us to avoid oversimplification of messages (see the livestock section) and on the other, framing calls for the integration of a plurality of values and worldviews (see the governance section). Thus, in the context of the UNFSS objective of transforming food systems, this brief aims to raise awareness of decision makers about the need of developing and using knowledge and tools that i) tackle the complexity of food systems as complex social-ecological systems, and ii) recognise the existence of different framing and values in a context of uncertainty. We also reflect on the role of science in this process. While our focus is global, we focus on Europe to exemplify our arguments. Yet, transformation cannot happen in one world region independently from the others. A global movement requires first adopters to start the process.

International audience; Decreasing enteric CH4 emissions is important in mitigating the environmental impact of livestock farming. The present meta-analysis examined effects of nutritional mitigation practices on absolute CH4 emissions (g/animal/d) and CH4 yield [g CH4/kg dry matter intake (DMI)] as well as on DMI (kg/d), average daily gain (kg/d), milk production (kg/d), and neutral detergent fiber digestibility (%). The database for this analysis consisted of over 400 studies. Only studies that reported statistical variance were included in the analysis (295 studies and 644 treatment mean comparisons). A standard random-effects meta-analysis weighted by inverse variance was carried out. The effects of the standardized mean difference (SMD) were classified as small (≤-0.2 and >-0.5), medium (≤-0.5 and >-0.8), and large (≤-0.8). Of the analyzed treatments, inclusion of chemical inhibitors, electron sinks, and lipids had a large effect on absolute CH4 emissions (-2.1 ± 0.5, -1.6 ± 0.2, and -1.3 ± 0.2 SMD ± SE, respectively; P 0.15), whereas electron sinks and lipids led to a small decrease in DMI (-0.2 ± 0.1, and -0.4 ± 0.1 SMD ± SE, respectively; P ≤0.01) without affecting animal productivity (P >0.05). Although these nutritional strategies effectively reduced CH4 emissions without compromising animal productivity, their adoption will largely depend on their economic feasibility.

Enteric methane (CH?) production from cattle contributes to global greenhouse gas emissions. Measurement of enteric CH? is complex, expensive and impractical at large scales; therefore, models are commonly used to predict CH? production. However, building robust prediction models requires extensive data from animals under different management systems worldwide. The objectives of this study were to (1) collate a global database of enteric CH? production from individual lactating dairy cattle; (2) determine the availability of key variables for predicting enteric CH? production (g/d per cow), yield [g/kg dry matter intake (DMI)], and intensity (g/kg energy corrected milk) and their respective relationships; (3) develop intercontinental and regional models and cross-validate their performance; and (4) assess the trade-off between availability of on-farm inputs and CH? prediction accuracy. The intercontinental database covered Europe (EU), the US (US), Chile (CL), Australia (AU), and New Zealand (NZ). A sequential approach was taken by incrementally adding key variables to develop models with increasing complexity. Methane emissions were predicted by fitting linear mixed models. Within model categories, an intercontinental model with the most available independent variables performed best with root mean square prediction error (RMSPE) as a percentage of mean observed value of 16.6, 14.4, and 19.8% for intercontinental, EU, and US regions, respectively. Less complex models requiring only DMI had predictive ability comparable to complex models. Enteric CH? production, yield, and intensity prediction models developed on an intercontinental basis had similar performance across regions, however, intercepts and slopes were different with implications for prediction. Revised CH? emission conversion factors for specific regions are required to improve CH? production estimates in national inventories. In conclusion, information on DMI is required for good prediction, and other factors such as dietary NDF concentration, improve the prediction. For enteric CH? yield and intensity prediction, information on milk yield and composition is required for better estimation.

Ruminant production systems are important contributors to anthropogenic methane (CH4) emissions, but there are large uncertainties in national and global livestock CH4 inventories. Sources of uncertainty in enteric CH4 emissions include animal inventories, feed dry matter intake (DMI), ingredient and chemical composition of the diets, and CH4 emission factors. There is also significant uncertainty associated with enteric CH4 measurements. The most widely used techniques are respiration chambers, the sulfur hexafluoride (SF6) tracer technique, and the automated head-chamber system (GreenFeed; C-Lock Inc., Rapid City, SD). All 3 methods have been successfully used in a large number of experiments with dairy or beef cattle in various environmental conditions, although studies that compare techniques have reported inconsistent results. Although different types of models have been developed to predict enteric CH4 emissions, relatively simple empirical (statistical) models have been commonly used for inventory purposes because of their broad applicability and ease of use compared with more detailed empirical and process-based mechanistic models. However, extant empirical models used to predict enteric CH4 emissions suffer from narrow spatial focus, limited observations, and limitations of the statistical technique used. Therefore, prediction models must be developed from robust data sets that can only be generated through collaboration of scientists across the world. To achieve high prediction accuracy, these data sets should encompass a wide range of diets and production systems within regions and globally. Overall, enteric CH4 prediction models are based on various animal or feed characteristic inputs but are dominated by DMI in one form or another. As a result, accurate prediction of DMI is essential for accurate prediction of livestock CH4 emissions. Analysis of a large data set of individual dairy cattle data showed that simplified enteric CH4 prediction models based on DMI alone or DMI and limited feed- or animal-related inputs can predict average CH4 emission with a similar accuracy to more complex empirical models. These simplified models can be reliably used for emission inventory purposes.

Ruminant production systems are important contributors to anthropogenic methane (CH4) emissions, but there are large uncertainties in national and global livestock CH4 inventories. Sources of uncertainty in enteric CH4 emissions include animal inventories, feed dry matter intake (DMI), ingredient and chemical composition of the diets, and CH4 emission factors. There is also significant uncertainty associated with enteric CH4 measurements. The most widely used techniques are respiration chambers, the sulfur hexafluoride (SF6) tracer technique, and the automated head-chamber system (GreenFeed; C-Lock Inc., Rapid City, SD). All 3 methods have been successfully used in a large number of experiments with dairy or beef cattle in various environmental conditions, although studies that compare techniques have reported inconsistent results. Although different types of models have been developed to predict enteric CH4 emissions, relatively simple empirical (statistical) models have been commonly used for inventory purposes because of their broad applicability and ease of use compared with more detailed empirical and process-based mechanistic models. However, extant empirical models used to predict enteric CH4 emissions suffer from narrow spatial focus, limited observations, and limitations of the statistical technique used. Therefore, prediction models must be developed from robust data sets that can only be generated through collaboration of scientists across the world. To achieve high prediction accuracy, these data sets should encompass a wide range of diets and production systems within regions and globally. Overall, enteric CH4 prediction models are based on various animal or feed characteristic inputs but are dominated by DMI in one form or another. As a result, accurate prediction of DMI is essential for accurate prediction of livestock CH4 emissions. Analysis of a large data set of individual dairy cattle data showed that simplified enteric CH4 prediction models based on DMI alone or DMI and limited feed- or animal-related inputs can predict average CH4 emission with a similar accuracy to more complex empirical models. These simplified models can be reliably used for emission inventory purposes., Journal of Dairy Science, 101 (7), ISSN:0022-0302, ISSN:1525-3198

Enteric methane (CH4) production from cattle contributes to global greenhouse gas emissions. Measurement of enteric CH4 is complex, expensive, and impractical at large scales; therefore, models are commonly used to predict CH4 production. However, building robust prediction models requires extensive data from animals under different management systems worldwide. The objectives of this study were to (1) collate a global database of enteric CH4 production from individual lactating dairy cattle; (2) determine the availability of key variables for predicting enteric CH4 production (g/day per cow), yield [g/kg dry matter intake (DMI)], and intensity (g/kg energy corrected milk) and their respective relationships; (3) develop intercontinental and regional models and cross‐validate their performance; and (4) assess the trade‐off between availability of on‐farm inputs and CH4 prediction accuracy. The intercontinental database covered Europe (EU), the United States (US), and Australia (AU). A sequential approach was taken by incrementally adding key variables to develop models with increasing complexity. Methane emissions were predicted by fitting linear mixed models. Within model categories, an intercontinental model with the most available independent variables performed best with root mean square prediction error (RMSPE) as a percentage of mean observed value of 16.6%, 14.7%, and 19.8% for intercontinental, EU, and United States regions, respectively. Less complex models requiring only DMI had predictive ability comparable to complex models. Enteric CH4 production, yield, and intensity prediction models developed on an intercontinental basis had similar performance across regions, however, intercepts and slopes were different with implications for prediction. Revised CH4 emission conversion factors for specific regions are required to improve CH4 production estimates in national inventories. In conclusion, information on DMI is required for good prediction, and other factors such as dietary neutral detergent fiber (NDF) concentration, improve the prediction. For enteric CH4 yield and intensity prediction, information on milk yield and composition is required for better estimation., Global Change Biology, 24 (8), ISSN:1354-1013, ISSN:1365-2486

International audience