SIMS(DAIRY): a modelling framework to identify sustainable dairy farms in the UK. Framework description and test for organic systems and N fertiliser optimisation

An integral assessment of the impact of diet and manure management on whole-farm greenhouse gas and nitrogen emissions in dairy cattle production systems using process-based models

Feed management decisions are crucial in mitigating greenhouse gas (GHG) and nitrogen (N) emissions from ruminant farming systems. However, assessing the downstream impact of diet on emissions in dairy production systems is complex, due to the multifunctional relationships between a variety of distinct but interconnected sources such as animals, housing, manure storage, and soil. Therefore, there is a need for an integral assessment of the direct and indirect GHG and N emissions that considers the underlying processes of carbon (C), N and their drivers within the system. Here we show the relevance of using a cascade of process-based (PB) models, such as Dutch Tier 3 and (Manure)-DNDC (Denitrification-Decomposition) models, for capturing the downstream influence of diet on whole-farm emissions in two contrasting case study dairy farms: a confinement system in Germany and a pasture-based system in New Zealand. Considerable variation was found in emissions on a per hectare and per head basis, and across different farm components and categories of animals. Moreover, the confinement system had a farm C emission of 1.01 kg CO2-eq kg-1 fat and protein corrected milk (FPCM), and a farm N emission of 0.0300 kg N kg-1 FPCM. In contrast, the pasture-based system had a lower farm C and N emission averaging 0.82 kg CO2-eq kg-1 FPCM and 0.006 kg N kg-1 FPCM, respectively over the 4-year period. The results demonstrate how inputs and outputs could be made compatible and exchangeable across the PB models for quantifying dietary effects on whole-farm GHG and N emissions.

Carbon dioxide equivalent emissions from corn silage fermentation

The European Climate Law recently codified the goal for European climate neutrality by 2050, highlighting the need for sustainable farming practices within a robust and transparent carbon dioxide equivalent (CO₂e) accounting system. In the present study, a series of equations were proposed for the estimation of CO₂e emissions from corn silage fermentation. Systematic review of previous meta-analyses of corn silage fermentation identified the mean and standard deviation statistics for key model inputs of acetic acid, ethanol, lactic acid, ammonia, and volatile-corrected dry matter loss. Estimates of CO₂e emissions were determined for a mock dataset comprising 1,000 iterations of randomly-generated values for each metric in accordance with mean and variance statistics of the source data. Estimates for CO₂e emissions of corn silage based on meta-analysis review of laboratory experiments were 1.9 ± 5.6% (GWP₂₀) and 0.2 ± 5.5% (GWP₁₀₀) of silage dry matter. Furthermore, model results demonstrated a precedent for CO₂ recycling by silage microorganisms, which was supported by genome annotation of strains belonging to common silage species. Linear model equations for GWP₂₀ and GWP₁₀₀ with inputs and outputs in mg kg^-1 silage dry matter were developed, where inputs are acetic acid (A), ethanol (E), lactic acid (L), and volatile corrected dry matter loss (D_V). Linear equations are (for GWP₂₀; Eq. 11): GWP 20 = - 3626.1 - 0.04343 A + 0.8011 E - 0.03173 L + 1.46573 D V and for GWP₁₀₀; Eq. 12: GWP 100 = - 8526.10 - 0.22403 A - 0.11963 E - 0.03173 L + 1.46573 D V . .

GHG Emissions from Dairy Small Ruminants in Castilla-La Mancha (Spain), Using the ManleCO2 Simulation Model

Simple Summary

Greenhouse gas emissions from ruminants contribute to global warming. “ManleCO₂” is an empirical model that simulates different management aspects in dairy sheep and goat farming, linking milk production to farming and environmental health. The carbon footprint of 1 L of fat- and protein-corrected milk varied from 2.01 to 5.62 kg CO₂e. Simulation scenarios showed a higher reduction in GHG emissions associated with animal feeding strategies and a lower reduction associated with farming management strategies. ManleCO₂ may provide useful information for planning and developing different strategies that might support the reduction of GHG emissions at the dairy sheep and goat farm level.

Abstract

The first goal of this work was the description of a model addressed to quantify the carbon footprint in Spanish autochthonous dairy sheep farms (Manchega group), foreign dairy sheep farms (foreigners group: Lacaune and Assaf breeds), and Spanish autochthonous dairy goat farms (Florida group). The second objective was to analyze the GHG emission mitigation potential of 17 different livestock farming practices that were implemented by 36 different livestock farms, in terms of CO₂e per hectare (ha), CO₂e per livestock unit (LU), and CO₂e per liter of fat- and protein-corrected milk (FPCM). The study showed the following results: 1.655 kg CO₂e per ha, 6.397 kg CO₂e per LU, and 3.78 kg CO₂e per liter of FPCM in the Manchega group; 12.634 kg CO₂e per ha, 7.810 CO₂e kg per LU, and 2.77 kg CO₂e per liter of FPCM in the Foreigners group and 1.198 kg CO₂e per ha, 6.507 kg CO₂e per LU, and 3.06 kg CO₂e per liter of FPCM in Florida group. In summary, purchasing off-farm animal feed would increase emissions by up to 3.86%. Conversely, forage management, livestock inventory, electrical supply, and animal genetic improvement would reduce emissions by up to 6.29%, 4.3%, 3.52%, and 0.8%, respectively; finally, an average rise of 2 °C in room temperature would increase emissions by up to 0.62%.

Evaluating Three-Pillar Sustainability Modelling Approaches for Dairy Cattle Production Systems

Milk production in Europe is facing major challenges to ensure its economic, environmental, and social sustainability. It is essential that holistic concepts are developed to ensure the future sustainability of the sector and to assist farmers and stakeholders in making knowledge-based decisions. In this study, integrated sustainability assessment by means of whole-farm modelling is presented as a valuable approach for identifying factors and mechanisms that could be used to improve the three pillars (3Ps) of sustainability in the context of an increasing awareness of economic profitability, social well-being, and environmental impacts of dairy production systems (DPS). This work aims (i) to create an evaluation framework that enables quantitative analysis of the level of integration of 3P sustainability indicators in whole-farm models and (ii) to test this method. Therefore, an evaluation framework consisting of 35 indicators distributed across the 3Ps of sustainability was used to evaluate three whole-farm models. Overall, the models integrated at least 40% of the proposed indicators. Different results were obtained for each sustainability pillar by each evaluated model. Higher scores were obtained for the environmental pillar, followed by the economic and the social pillars. In conclusion, this evaluation framework was found to be an effective tool that allows potential users to choose among whole-farm models depending on their needs. Pathways for further model development that may be used to integrate the 3P sustainability assessment of DPS in a more complete and detailed way were identified.

Challenges and opportunities to capture dietary effects in on-farm greenhouse gas emissions models of ruminant systems

This paper reviews existing on-farm GHG accounting models for dairy cattle systems and their ability to capture the effect of dietary strategies in GHG abatement. The focus is on methane (CH₄) emissions from enteric and manure (animal excreta) sources and nitrous oxide (N₂O) emissions from animal excreta. We identified three generic modelling approaches, based on the degree to which models capture diet-related characteristics: from 'none' (Type 1) to 'some' by combining key diet parameters with emission factors (EF) (Type 2) to 'many' by using process-based modelling (Type 3). Most of the selected on-farm GHG models have adopted a Type 2 approach, but a few hybrid Type 2 / Type 3 approaches have been developed recently that combine empirical modelling (through the use of CH₄ and/or N₂O emission factors; EF) and process-based modelling (mostly through rumen and whole tract fermentation and digestion). Empirical models comprising key dietary inputs (i.e., dry matter intake and organic matter digestibility) can predict CH₄ and N₂O emissions with reasonable accuracy. However, the impact of GHG mitigation strategies often needs to be assessed in a more integrated way, and Type 1 and Type 2 models frequently lack the biological foundation to do this. Only Type 3 models represent underlying mechanisms such as ruminal and total-tract digestive processes and excreta composition that can capture dietary effects on GHG emissions in a more biological manner. Overall, the better a model can simulate rumen function, the greater the opportunity to include diet characteristics in addition to commonly used variables, and thus the greater the opportunity to capture dietary mitigation strategies. The value of capturing the effect of additional animal feed characteristics on the prediction of on-farm GHG emissions needs to be carefully balanced against gains in accuracy, the need for additional input and activity data, and the variability encountered on-farm.

The Ruminant Farm Systems Animal Module: A Biophysical Description of Animal Management

Dairy production is an important source of nutrients in the global food supply, but environmental impacts are increasingly a concern of consumers, scientists, and policy-makers. Many decisions must be integrated to support sustainable production-which can be achieved using a simulation model. We provide an example of the Ruminant Farm Systems (RuFaS) model to assess changes in the dairy system related to altered animal feed efficiency. RuFaS is a whole-system farm simulation model that simulates the individual animal life cycle, production, and environmental impacts. We added a stochastic animal-level parameter to represent individual animal feed efficiency as a result of reduced residual feed intake and compared High (intake = 94% of expected) and Very High (intake = 88% of expected) efficiency levels with a Baseline scenario (intake = 100% of expected). As expected, the simulated total feed intake was reduced by 6 and 12% for the High and Very High efficiency scenarios, and the expected impact of these improved efficiencies on the greenhouse gas emissions from enteric methane and manure storage was a decrease of 4.6 and 9.3%, respectively.

Sustainability Assessment of Pasture-Based Dairy Sheep Systems: A Multidisciplinary and Multiscale Approach

This article describes a novel methodological approach for the integrated sustainability assessment of pasture-based dairy sheep systems. Most studies on livestock system sustainability focus on animal production, farm profitability, and mitigation strategies of greenhouse gas emissions. However, recent research indicates that pasture-based livestock farming also contributes positively to rural areas, and the associated increase in plant diversity promotes ecosystem functioning and services in natural and managed grasslands. Likewise, little attention has focused on how pasture-based livestock systems affect soil carbon changes, biodiversity, and ecotoxicity. Furthermore, the quality and safety of food products, particularly sheep milk and cheese, and socioeconomic issues such as cultural heritage and consumer behavior are often neglected in livestock system sustainability assessments. To improve the analysis of sustainability and adaptation strategies of livestock systems, we suggest a holistic approach that integrates indicators from diverse disciplines with complementary methods and models capable of capturing the complexity of these systems at multiple scales. A multidisciplinary perspective generates new indicators to identify critical trade-offs and synergies related to the resilience of dairy sheep livestock systems. A multiscale approach provides insights on the effects of socioeconomic and environmental changes associated with current dairy sheep grazing systems across multiple scales. The combined approach will facilitate the development and progressive implementation of novel management strategies needed to adapt pasture-based dairy sheep farms to changing conditions under future socioeconomic and environmental scenarios.

Carbon Footprint Assessment of Spanish Dairy Cattle Farms: Effectiveness of Dietary and Farm Management Practices as a Mitigation Strategy

Simple Summary

Livestock production has been identified as an important source of greenhouse gas emissions. The current study was conducted to quantify the carbon footprint of Spanish dairy farms and to evaluate the potential of nutritional and management practices for mitigating methane emissions at farm level. The carbon footprint ranged from 0.67 to 0.98 kg CO₂-eq/kg of energy corrected milk. Simulation scenarios showed that methane emissions and the carbon footprint of milk could be reduced more through management practices rather than dietary strategies. Modelling may provide policy makers, farmers and stakeholders valuable information for planning and developing strategies to reduce the carbon footprint associated with milk production.

Abstract

Greenhouse gas emissions and the carbon footprint (CF) were estimated in twelve Spanish dairy farms selected from three regions (Mediterranean, MED; Cantabric, CAN; and Central, CEN) using a partial life cycle assessment through the Integrated Farm System Model (IFSM). The functional unit was 1 kg of energy corrected milk (ECM). Methane emissions accounted for the largest contribution to the total greenhouse gas (GHG) emissions. The average CF (kg CO₂-eq/kg of ECM) was 0.84, being the highest in MED (0.98), intermediate in CEN (0.84), and the lowest in CAN (0.67). Two extreme farms were selected for further simulations: one with the highest non-enteric methane (MED1), and another with the highest enteric methane (CAN2). Changes in management scenarios (increase milk production, change manure collection systems, change manure-type storage method, change bedding type and installation of an anaerobic digester) in MED1 were evaluated with the IFSM model. Changes in feeding strategies (reduce the forage: concentrate ratio, improve forage quality, use of ionophores) in CAN2 were evaluated with the Cornell Net Carbohydrate and Protein System model. Results indicate that changes in management (up to 27.5% reduction) were more efficient than changes in dietary practices (up to 3.5% reduction) in reducing the carbon footprint.

Climate Change Impact, Adaptation, and Mitigation in Temperate Grazing Systems: A Review

Managed temperate grasslands occupy 25% of the world, which is 70% of global agricultural land. These lands are an important source of food for the global population. This review paper examines the impacts of climate change on managed temperate grasslands and grassland-based livestock and effectiveness of adaptation and mitigation options and their interactions. The paper clarifies that moderately elevated atmospheric CO2 (eCO2) enhances photosynthesis, however it may be restiricted by variations in rainfall and temperature, shifts in plant’s growing seasons, and nutrient availability. Different responses of plant functional types and their photosynthetic pathways to the combined effects of climatic change may result in compositional changes in plant communities, while more research is required to clarify the specific responses. We have also considered how other interacting factors, such as a progressive nitrogen limitation (PNL) of soils under eCO2, may affect interactions of the animal and the environment and the associated production. In addition to observed and modelled declines in grasslands productivity, changes in forage quality are expected. The health and productivity of grassland-based livestock are expected to decline through direct and indirect effects from climate change. Livestock enterprises are also significant cause of increased global greenhouse gas (GHG) emissions (about 14.5%), so climate risk-management is partly to develop and apply effective mitigation measures. Overall, our finding indicates complex impact that will vary by region, with more negative than positive impacts. This means that both wins and losses for grassland managers can be expected in different circumstances, thus the analysis of climate change impact required with potential adaptations and mitigation strategies to be developed at local and regional levels.

Food Footprint as a Measure of Sustainability for Grazing Dairy Farms

Livestock productions require significant resources allocation in the form of land, water, energy, air, and capital. Meanwhile, owing to increase in the global demand for livestock products, it is wise to consider sustainable livestock practices. In the past few decades, footprints have emerged as indicators for sustainability assessment. In this study, we are introducing a new footprint measure to assess sustainability of a grazing dairy farm while considering carbon, water, energy, and economic impacts of milk production. To achieve this goal, a representative farm was developed based on grazing dairy practices surveys in the State of Michigan, USA. This information was incorporated into the Integrated Farm System Model (IFSM) to estimate the farm carbon, water, energy, and economic impacts and associated footprints for ten different regions in Michigan. A multi-criterion decision-making method called VIKOR was used to determine the overall impacts of the representative farms. This new measure is called the food footprint. Using this new indicator, the most sustainable milk production level (8618 kg/cow/year) was identified that is 19.4% higher than the average milk production (7215 kg/cow/year) in the area of interest. In addition, the most sustainable pasture composition was identified as 90% tall fescue with 10% white clover. The methodology introduced here can be adopted in other regions to improve sustainability by reducing water, energy, and environmental impacts of grazing dairy farms, while maximizing the farm profit and productions.

A systematic review of non-productivity-related animal-based indicators of heat stress resilience in dairy cattle

INTRODUCTION

Projected temperature rise in the upcoming years due to climate change has increased interest in studying the effects of heat stress in dairy cows. Environmental indices are commonly used for detecting heat stress, but have been used mainly in studies focused on the productivity-related effects of heat stress. The welfare approach involves identifying physiological and behavioural measurements so as to start heat stress mitigation protocols before the appearance of impending severe health or production issues. Therefore, there is growing interest in studying the effects of heat stress on welfare. This systematic review seeks to summarise the animal-based responses to heat stress (physiological and behavioural, excluding productivity) that have been used in scientific literature.

METHODS

Using systematic review guidelines set by PRISMA, research articles were identified, screened and summarised based on inclusion criteria for physiology and behaviour, excluding productivity, for animal-based resilience indicators. 129 published articles were reviewed to determine which animal-based indicators for heat stress were most frequently used in dairy cows.

RESULTS

The articles considered report at least 212 different animal-based indicators that can be aggregated into body temperature, feeding, physiological response, resting, drinking, grazing and pasture-related behaviour, reactions to heat management and others. The most common physiological animal-based indicators are rectal temperature, respiration rate and dry matter intake, while the most common behavioural indicators are time spent lying, standing and feeding.

CONCLUSION

Although body temperature and respiration rate are the animal-based indicators most frequently used to assess heat stress in dairy cattle, when choosing an animal-based indicator for detecting heat stress using scientific literature to establish thresholds, characteristics that influence the scale of the response and the definition of heat stress must be taken into account, e.g. breed, lactation stage, milk yield, system type, climate region, bedding type, diet and cooling management strategies.

The landscape model: A model for exploring trade-offs between agricultural production and the environment

We describe a model framework that simulates spatial and temporal interactions in agricultural landscapes and that can be used to explore trade-offs between production and environment so helping to determine solutions to the problems of sustainable food production. Here we focus on models of agricultural production, water movement and nutrient flow in a landscape. We validate these models against data from two long-term experiments, (the first a continuous wheat experiment and the other a permanent grass-land experiment) and an experiment where water and nutrient flow are measured from isolated catchments. The model simulated wheat yield (RMSE 20.3-28.6%), grain N (RMSE 21.3-42.5%) and P (RMSE 20.2-29% excluding the nil N plots), and total soil organic carbon particularly well (RMSE3.1-13.8%), the simulations of water flow were also reasonable (RMSE 180.36 and 226.02%). We illustrate the use of our model framework to explore trade-offs between production and nutrient losses.

Framework for integrating animal welfare into life cycle sustainability assessment

PURPOSE

This study seeks to provide a framework for integrating animal welfare as a fourth pillar into a life cycle sustainability assessment and presents three alternative animal welfare indicators.

METHODS

Animal welfare is assessed during farm life and during slaughter. The indicators differ in how they value premature death. All three consider (1) the life quality of an animal such as space allowance, (2) the slaughter age either as life duration or life fraction, and (3) the number of animals affected for providing a product unit, e.g. 1 Mcal. One of the indicators additionally takes into account a moral value denoting their intelligence and self-awareness. The framework allows for comparisons across studies and products and for applications at large spatial scales. To illustrate the framework, eight products were analysed and compared: beef, pork, poultry, milk, eggs, salmon, shrimps, and, as a novel protein source, insects.

RESULTS AND DISCUSSION

Insects are granted to live longer fractions of their normal life spans, and their life quality is less compromised due to a lower assumed sentience. Still, they perform worst according to all three indicators, as their small body sizes only yield low product quantities. Therefore, we discourage from eating insects. In contrast, milk is the product that reduces animal welfare the least according to two of the three indicators and it performs relatively better than other animal products in most categories. The difference in animal welfare is mostly larger for different animal products than for different production systems of the same product. This implies that, besides less consumption of animal-based products, a shift to other animal products can significantly improve animal welfare.

CONCLUSIONS

While the animal welfare assessment is simplified, it allows for a direct integration into life cycle sustainability assessment. There is a trade-off between applicability and indicator complexity, but even a simple estimate of animal welfare is much better than ignoring the issue, as is the common practice in life cycle sustainability assessments. Future research should be directed towards elaborating the life quality criterion and extending the product coverage.

Animal Board Invited Review: Comparing conventional and organic livestock production systems on different aspects of sustainability

To sustainably contribute to food security of a growing and richer world population, livestock production systems are challenged to increase production levels while reducing environmental impact, being economically viable, and socially responsible. Knowledge about the sustainability performance of current livestock production systems may help to formulate strategies for future systems. Our study provides a systematic overview of differences between conventional and organic livestock production systems on a broad range of sustainability aspects and animal species available in peer-reviewed literature. Systems were compared on economy, productivity, environmental impact, animal welfare and public health. The review was limited to dairy cattle, beef cattle, pigs, broilers and laying hens, and to Europe, North America and New Zealand. Results per indicators are presented as in the articles without performing additional calculations. Out of 4171 initial search hits, 179 articles were analysed. Studies varied widely in indicators, research design, sample size and location and context. Quite some studies used small samples. No study analysed all aspects of sustainability simultaneously. Conventional systems had lower labour requirements per unit product, lower income risk per animal, higher production per animal per time unit, higher reproduction numbers, lower feed conversion ratio, lower land use, generally lower acidification and eutrophication potential per unit product, equal or better udder health for cows and equal or lower microbiological contamination. Organic systems had higher income per animal or full time employee, lower impact on biodiversity, lower eutrophication and acidification potential per unit land, equal or lower likelihood of antibiotic resistance in bacteria and higher beneficial fatty acid levels in cow milk. For most sustainability aspects, sometimes conventional and sometimes organic systems performed better, except for productivity, which was consistently higher in conventional systems. For many aspects and animal species, more data are needed to conclude on a difference between organic and conventional livestock production systems.

SIMSWASTE-AD - A modelling framework for the environmental assessment of agricultural waste management strategies: Anaerobic digestion

On-farm anaerobic digestion (AD) has been promoted due to its improved environmental performance, which is based on a number of life cycle assessments (LCA). However, the influence of site-specific conditions and practices on AD performance is rarely captured in LCA studies and the effects on C and N cycles are often overlooked. In this paper, a new model for AD (SIMS_WASTE-AD) is described in full and tested against a selection of available measured data. Good agreement between modelled and measured values was obtained, reflecting the model capability to predict biogas production (r²=0.84) and N mineralization (r²=0.85) under a range of substrate mixtures and operational conditions. SIMS_WASTE-AD was also used to simulate C and N flows and GHG emissions for a set of scenarios exploring different AD technology levels, feedstock mixtures and climate conditions. The importance of post-digestion emissions and its relationship with the AD performance have been stressed as crucial factors to reduce the net GHG emissions (-75%) but also to enhance digestate fertilizer potential (15%). Gas tight digestate storage with residual biogas collection is highly recommended (especially in temperate to warm climates), as well as those operational conditions that can improve the process efficiency on degrading VS (e.g. thermophilic range, longer hydraulic retention time). Beyond the effects on the manure management stage, SIMS_WASTE-AD also aims to help account for potential effects of AD on other stages by providing the C and nutrient flows. While primarily designed to be applied within the SIMS_DAIRY modelling framework, it can also interact with other models implemented in integrated approaches. Such system scope assessments are essential for stakeholders and policy makers in order to develop effective strategies for reducing GHG emissions and environmental issues in the agriculture sector.

Modelling the cost-effectiveness of mitigation methods for multiple pollutants at farm scale

Reductions in agricultural pollution are essential for meeting nationally and internationally agreed policy targets for losses to both air and water. Numerous studies quantify the impact of relevant mitigation methods by field experimentation or computer modelling. The majority of these studies have addressed individual methods and frequently also individual pollutants. This paper presents a conceptual model for the synthesis of the evidence base to calculate the impact of multiple methods addressing multiple pollutants in order to identify least cost solutions for multiple policy objectives. The model is implemented as a farm scale decision support tool that quantifies baseline pollutant losses for identifiable sources, areas and pathways and incorporates a genetic algorithm based multi-objective procedure for determining optimal suites of mitigation methods. The tool is generic as baseline losses can be replaced with measured data and the default library of mitigation methods can be edited and expanded. The tool is demonstrated through application to two contrasting farm systems, using survey data on agricultural practices typical of England and Wales. These examples show how the tool could be used to help target the adoption of mitigation options for the control of diffuse pollution from agriculture. The feedback from workshops where Farmscoper was demonstrated is included to highlight the potential role of Farmscoper as part of the farm advisory process.

Whole-farm models to quantify greenhouse gas emissions and their potential use for linking climate change mitigation and adaptation in temperate grassland ruminant-based farming systems

The farm level is the most appropriate scale for evaluating options for mitigating greenhouse gas (GHG) emissions, because the farm represents the unit at which management decisions in livestock production are made. To date, a number of whole farm modelling approaches have been developed to quantify GHG emissions and explore climate change mitigation strategies for livestock systems. This paper analyses the limitations and strengths of the different existing approaches for modelling GHG mitigation by considering basic model structures, approaches for simulating GHG emissions from various farm components and the sensitivity of GHG outputs and mitigation measures to different approaches. Potential challenges for linking existing models with the simulation of impacts and adaptation measures under climate change are explored along with a brief discussion of the effects on other ecosystem services.

Modelling the interactions between C and N farm balances and GHG emissions from confinement dairy farms in northern Spain

There is world-wide concern for the contribution of dairy farming to global warming. However, there is still a need to improve the quantification of the C-footprint of dairy farming systems under different production systems and locations since most of the studies (e.g. at farm-scale or using LCA) have been carried out using too simplistic and generalised approaches. A modelling approach integrating existing and new sub-models has been developed and used to simulate the C and N flows and to predict the GHG burden of milk production (from the cradle to the farm gate) from 17 commercial confinement dairy farms in the Basque Country (northern Spain). We studied the relationship between their GHG emissions, and their management and economic performance. Additionally, we explored some of the effects on the GHG results of the modelling methodology choice. The GHG burden values resulting from this study (0.84-2.07 kg CO2-eq kg(-l) milk ECM), although variable, were within the range of values of existing studies. It was evidenced, however, that the methodology choice used for prediction had a large effect on the results. Methane from the rumen and manures, and N2O emissions from soils comprised most of the GHG emissions for milk production. Diet was the strongest factor explaining differences in GHG emissions from milk production. Moreover, the proportion of feed from the total cattle diet that could have directly been used to feed humans (e.g. cereals) was a good indicator to predict the C-footprint of milk. Not only were some other indicators, such as those in relation with farm N use efficiency, good proxies to estimate GHG emissions per ha or per kg milk ECM (C-footprint of milk) but they were also positively linked with farm economic performance.

Modelling Farm Animal Welfare

The use of models in the life sciences has greatly expanded in scope and advanced in technique in recent decades. However, the range, type and complexity of models used in farm animal welfare is comparatively poor, despite the great scope for use of modeling in this field of research. In this paper, we review the different modeling approaches used in farm animal welfare science to date, discussing the types of questions they have been used to answer, the merits and problems associated with the method, and possible future applications of each technique. We find that the most frequently published types of model used in farm animal welfare are conceptual and assessment models; two types of model that are frequently (though not exclusively) based on expert opinion. Simulation, optimization, scenario, and systems modeling approaches are rarer in animal welfare, despite being commonly used in other related fields. Finally, common issues such as a lack of quantitative data to parameterize models, and model selection and validation are discussed throughout the review, with possible solutions and alternative approaches suggested.

Life cycle assessment of milk production from commercial dairy farms: the influence of management tactics

Little consideration has been given to how farm management, specifically tactics used to implement the management strategy, may influence the carbon footprint (CF) and land use for milk produced on commercial farms. In this study, the CF and land use of milk production from 18 Irish commercial dairy farms were analyzed based on foreground data from a 12-mo survey capturing management tactics and background data from the literature. Large variation was found in farm attributes and management tactics; for example, up to a 1.5-fold difference in fertilizer nitrogen input was used to support the same stocking density, and up to a 3.5-fold difference in concentrate fed for similar milk output per cow. However, the coefficient of variation for milk CF between farms only varied by 13% and for land use by 18%. The overall CF and overall land use of the milk production from the 18 dairy farms was 1.23±0.04kg of CO2 Eq and 1.22±0.05 m(2) per kilogram of energy-corrected milk. Milk output per cow, economic allocation between exports of milk and liveweight, and on-farm diesel use per ha were found to be influential factors on milk CF, whereas the fertilizer N rate, milk output per cow, and economic allocation between exports of milk and liveweight were influential on land use. Effective sward management of white clover within a few farms appeared to lower the CF but increased on-farm land use. It was concluded that a combination of multiple tactics determines CF and land use for milk production on commercial dairy farms and, although these 2 measures of environmental impact are correlated, a farm with a low CF did not always have low land use and vice versa.