Reducing the Environmental Impact of Animal Production

La demanda mundial de alimentos aumentará en los próximos 30 años para satisfacer las necesidades de la creciente población. Es probable que la demanda de productos de origen animal (carne, leche y huevos) aumente a un ritmo más rápido que la demanda de cereales. Hay presión para reducir los impactos ambientales de los sistemas pecuarios, en particular las emisiones de gases de efecto invernadero (GEI) y la excreción de nitrógeno y fósforo. El principal impulsor del impacto ambiental en los sistemas animales es la eficiencia de la producción, es decir, la producción de leche, carne, huevos o contaminantes por unidad de insumo. La eficiencia de producción está relacionada con el rendimiento por animal, la tasa de reproducción y la tasa de reemplazo. Una mayor eficiencia de producción significa que se necesitan menos animales por unidad de producto, de modo que las emisiones y excreciones "improductivas" asociadas con el mantenimiento y la fase de crianza se distribuyen en más unidades de producto. La nutrición puede reducir las emisiones y las excreciones por animal. Las emisiones de metano de los rumiantes están relacionadas con la cantidad de forraje digerido, por lo que aumentar las proporciones de la dieta de los concentrados y aumentar el contenido de almidón o grasa a expensas de la fibra, reducirá el metano por unidad de producto. La selección genética para bajas emisiones de metano solo debe considerarse junto con la eficiencia de la alimentación. Las excreciones de nitrógeno y fósforo están relacionadas con el contenido de nitrógeno y fósforo en la dieta, particularmente con exceso de suministros. La formulación precisa de la dieta, el uso de requisitos proteicos degradables e indestructibles del rumen en los rumiantes y los requisitos de aminoácidos digestibles en los no rumiantes, puede reducir la excreción de nitrógeno. La reducción del contenido de fósforo en las dietas y las enzimas fitasas en las dietas no rumiantes pueden reducir la excreción de fósforo. En conclusión, la principal estrategia para reducir la huella ambiental de los sistemas pecuarios debe ser reducir el desperdicio de animales reproductores mediante el sacrificio prematuro de fertilidad y enfermedades. Esto también mejorará la rentabilidad. Por lo tanto, se necesita un enfoque de todo el sistema que considere el costo ambiental de la formulación de la dieta, así como el costo económico.Global food demand will increase in the next 30 years to meet the needs of the increasing population. Demand for animal products (meat, milk and eggs) is likely to increase at a faster rate than the demand for cereals. There is pressure to reduce the environmental impacts of livestock systems, particularly greenhouse gas emissions (GHG) and excretion of nitrogen and phosphorus. The main driver of environmental impact in animal systems is production efficiency, i.e. output of milk, meat, eggs or pollutants per unit of input. Production efficiency is related to performance per animal, reproductive rate and replacement rate. Higher production efficiency means that fewer animals are needed per unit of the product so that ‘unproductive’ emissions and excretions associated with maintenance and the rearing phase are spread over more units of product. Nutrition can reduce emissions and excretions per animal. Methane emissions by ruminants are related to the quantity of forage digested, so increasing dietary proportions of concentrates, and increasing starch or fat content at the expense of fibre will reduce methane per unit of product. Genetic selection for low methane emissions should only be considered alongside feed efficiency. Nitrogen and phosphorus excretions are related to dietary nitrogen and phosphorus contents, particularly with excess supplies. Precise diet formulation, using rumen degradable and undegradable protein requirements in ruminants, and digestible amino acid requirements in non-ruminants, can reduce nitrogen excretion. Reducing phosphorus content of diets, and phytase enzymes in non-ruminant diets can reduce phosphorus excretion. In conclusion, the main strategy for reducing the environmental footprint of livestock systems must be to reduce wastage of breeding animals through premature culling for fertility and diseases. This will also improve profitability. Therefore, a whole-system approach is needed which considers the environmental cost of diet formulation as well as economic cost

Prediction of enteric methane production, yield and intensity in dairy cattle using an intercontinental database

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/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 CH4 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 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 NDF concentration, improve the prediction. For enteric CH4 yield and intensity prediction, information on milk yield and composition is required for better estimation

Where have we come with breeding for methane emissions : update from international collaborations

Where have we come with breeding for methane emissions – update from international collaborations Climate change is a growing international concern and it is well established that release of greenhouse gases (GHG) is a contributing factor. So far, within animal production, there is little or no concerted effort on long-term breeding strategies to mitigate GHG from ruminants. In recent years, several consortia have been formed to collect and combine data for genetic evaluation. Discussion areas of these consortia focus on (1) What are genetic parameters for methane (CH4) emissions, (2) What proxies can be used to assess CH4 emission, and (3) What are the prospects of breeding for lower emitting animals? The estimated genetic parameters show that enteric CH4 is a heritable trait, and that it is highly genetically correlated with DMI. So far, the most useful proxies relate to feed intake, milk mid-infrared spectral data, and fatty acid concentrations in milk. To be able to move forward with a genetic evaluation and ranking of animals for CH4 emission, international collaboration is essential to make progress in this area. Collaboration is not only in terms of sharing ideas, experiences and phenotypes, but also in terms of coming to a consensus regarding what phenotype to collect and to select for. Keywords: greenhouse gas emission, enteric methane, genetic contro

Where have we come with breeding for methane emissions : update from international collaborations

Where have we come with breeding for methane emissions – update from international collaborations Climate change is a growing international concern and it is well established that release of greenhouse gases (GHG) is a contributing factor. So far, within animal production, there is little or no concerted effort on long-term breeding strategies to mitigate GHG from ruminants. In recent years, several consortia have been formed to collect and combine data for genetic evaluation. Discussion areas of these consortia focus on (1) What are genetic parameters for methane (CH4) emissions, (2) What proxies can be used to assess CH4 emission, and (3) What are the prospects of breeding for lower emitting animals? The estimated genetic parameters show that enteric CH4 is a heritable trait, and that it is highly genetically correlated with DMI. So far, the most useful proxies relate to feed intake, milk mid-infrared spectral data, and fatty acid concentrations in milk. To be able to move forward with a genetic evaluation and ranking of animals for CH4 emission, international collaboration is essential to make progress in this area. Collaboration is not only in terms of sharing ideas, experiences and phenotypes, but also in terms of coming to a consensus regarding what phenotype to collect and to select for. Keywords: greenhouse gas emission, enteric methane, genetic contro

Data_Sheet_2_Taxonomic and predicted functional signatures reveal linkages between the rumen microbiota and feed efficiency in dairy cattle raised in tropical areas.xlsx

Ruminants digest plant biomass more efficiently than monogastric animals due to their symbiotic relationship with a complex microbiota residing in the rumen environment. What remains unclear is the relationship between the rumen microbial taxonomic and functional composition and feed efficiency (FE), especially in crossbred dairy cattle (Holstein x Gyr) raised under tropical conditions. In this study, we selected twenty-two F1 Holstein x Gyr heifers and grouped them according to their residual feed intake (RFI) ranking, high efficiency (HE) (n = 11) and low efficiency (LE) (n = 11), to investigate the effect of FE on the rumen microbial taxa and their functions. Rumen fluids were collected using a stomach tube apparatus and analyzed using amplicon sequencing targeting the 16S (bacteria and archaea) and 18S (protozoa) rRNA genes. Alpha-diversity and beta-diversity analysis revealed no significant difference in the rumen microbiota between the HE and LE animals. Multivariate analysis (sPLS-DA) showed a clear separation of two clusters in bacterial taxonomic profiles related to each FE group, but in archaeal and protozoal profiles, the clusters overlapped. The sPLS-DA also revealed a clear separation in functional profiles for bacteria, archaea, and protozoa between the HE and LE animals. Microbial taxa were differently related to HE (e.g., Howardella and Shuttleworthia) and LE animals (e.g., Eremoplastron and Methanobrevibacter), and predicted functions were significatively different for each FE group (e.g., K03395—signaling and cellular process was strongly related to HE animals, and K13643—genetic information processing was related to LE animals). This study demonstrates that differences in the rumen microbiome relative to FE ranking are not directly observed from diversity indices (Faith’s Phylogenetic Diversity, Pielou’s Evenness, Shannon’s diversity, weighted UniFrac distance, Jaccard index, and Bray–Curtis dissimilarity), but from targeted identification of specific taxa and microbial functions characterizing each FE group. These results shed light on the role of rumen microbial taxonomic and functional profiles in crossbred Holstein × Gyr dairy cattle raised in tropical conditions, creating the possibility of using the microbial signature of the HE group as a biological tool for the development of biomarkers that improve FE in ruminants.</p

Data_Sheet_3_Taxonomic and predicted functional signatures reveal linkages between the rumen microbiota and feed efficiency in dairy cattle raised in tropical areas.xlsx

Ruminants digest plant biomass more efficiently than monogastric animals due to their symbiotic relationship with a complex microbiota residing in the rumen environment. What remains unclear is the relationship between the rumen microbial taxonomic and functional composition and feed efficiency (FE), especially in crossbred dairy cattle (Holstein x Gyr) raised under tropical conditions. In this study, we selected twenty-two F1 Holstein x Gyr heifers and grouped them according to their residual feed intake (RFI) ranking, high efficiency (HE) (n = 11) and low efficiency (LE) (n = 11), to investigate the effect of FE on the rumen microbial taxa and their functions. Rumen fluids were collected using a stomach tube apparatus and analyzed using amplicon sequencing targeting the 16S (bacteria and archaea) and 18S (protozoa) rRNA genes. Alpha-diversity and beta-diversity analysis revealed no significant difference in the rumen microbiota between the HE and LE animals. Multivariate analysis (sPLS-DA) showed a clear separation of two clusters in bacterial taxonomic profiles related to each FE group, but in archaeal and protozoal profiles, the clusters overlapped. The sPLS-DA also revealed a clear separation in functional profiles for bacteria, archaea, and protozoa between the HE and LE animals. Microbial taxa were differently related to HE (e.g., Howardella and Shuttleworthia) and LE animals (e.g., Eremoplastron and Methanobrevibacter), and predicted functions were significatively different for each FE group (e.g., K03395—signaling and cellular process was strongly related to HE animals, and K13643—genetic information processing was related to LE animals). This study demonstrates that differences in the rumen microbiome relative to FE ranking are not directly observed from diversity indices (Faith’s Phylogenetic Diversity, Pielou’s Evenness, Shannon’s diversity, weighted UniFrac distance, Jaccard index, and Bray–Curtis dissimilarity), but from targeted identification of specific taxa and microbial functions characterizing each FE group. These results shed light on the role of rumen microbial taxonomic and functional profiles in crossbred Holstein × Gyr dairy cattle raised in tropical conditions, creating the possibility of using the microbial signature of the HE group as a biological tool for the development of biomarkers that improve FE in ruminants.</p

Data_Sheet_1_Taxonomic and predicted functional signatures reveal linkages between the rumen microbiota and feed efficiency in dairy cattle raised in tropical areas.docx

Ruminants digest plant biomass more efficiently than monogastric animals due to their symbiotic relationship with a complex microbiota residing in the rumen environment. What remains unclear is the relationship between the rumen microbial taxonomic and functional composition and feed efficiency (FE), especially in crossbred dairy cattle (Holstein x Gyr) raised under tropical conditions. In this study, we selected twenty-two F1 Holstein x Gyr heifers and grouped them according to their residual feed intake (RFI) ranking, high efficiency (HE) (n = 11) and low efficiency (LE) (n = 11), to investigate the effect of FE on the rumen microbial taxa and their functions. Rumen fluids were collected using a stomach tube apparatus and analyzed using amplicon sequencing targeting the 16S (bacteria and archaea) and 18S (protozoa) rRNA genes. Alpha-diversity and beta-diversity analysis revealed no significant difference in the rumen microbiota between the HE and LE animals. Multivariate analysis (sPLS-DA) showed a clear separation of two clusters in bacterial taxonomic profiles related to each FE group, but in archaeal and protozoal profiles, the clusters overlapped. The sPLS-DA also revealed a clear separation in functional profiles for bacteria, archaea, and protozoa between the HE and LE animals. Microbial taxa were differently related to HE (e.g., Howardella and Shuttleworthia) and LE animals (e.g., Eremoplastron and Methanobrevibacter), and predicted functions were significatively different for each FE group (e.g., K03395—signaling and cellular process was strongly related to HE animals, and K13643—genetic information processing was related to LE animals). This study demonstrates that differences in the rumen microbiome relative to FE ranking are not directly observed from diversity indices (Faith’s Phylogenetic Diversity, Pielou’s Evenness, Shannon’s diversity, weighted UniFrac distance, Jaccard index, and Bray–Curtis dissimilarity), but from targeted identification of specific taxa and microbial functions characterizing each FE group. These results shed light on the role of rumen microbial taxonomic and functional profiles in crossbred Holstein × Gyr dairy cattle raised in tropical conditions, creating the possibility of using the microbial signature of the HE group as a biological tool for the development of biomarkers that improve FE in ruminants.</p

Prediction of enteric methane production, yield, and intensity in dairy cattle using an intercontinental database.

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

Challenges and priorities for modelling livestock health and pathogens in the context of climate change

Climate change has the potential to impair livestock health, with consequences for animal welfare, productivity, greenhouse gas emissions, and human livelihoods and health. Modelling has an important role in assessing the impacts of climate change on livestock systems and the efficacy of potential adaptation strategies, to support decision making for more efficient, resilient and sustainable production. However, a coherent set of challenges and research priorities for modelling livestock health and pathogens under climate change has not previously been available. To identify such challenges and priorities, researchers from across Europe were engaged in a horizon-scanning study, involving workshop and questionnaire based exercises and focussed literature reviews. Eighteen key challenges were identified and grouped into six categories based on subject-specific and capacity building requirements. Across a number of challenges, the need for inventories relating model types to different applications (e.g. the pathogen species, region, scale of focus and purpose to which they can be applied) was identified, in order to identify gaps in capability in relation to the impacts of climate change on animal health. The need for collaboration and learning across disciplines was highlighted in several challenges, e.g. to better understand and model complex ecological interactions between pathogens, vectors, wildlife hosts and livestock in the context of climate change. Collaboration between socio-economic and biophysical disciplines was seen as important for better engagement with stakeholders and for improved modelling of the costs and benefits of poor livestock health. The need for more comprehensive validation of empirical relationships, for harmonising terminology and measurements, and for building capacity for under-researched nations, systems and health problems indicated the importance of joined up approaches across nations. The challenges and priorities identified can help focus the development of modelling capacity and future research structures in this vital field. Well-funded networks capable of managing the long-term development of shared resources are required in order to create a cohesive modelling community equipped to tackle the complex challenges of climate change