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Global greenhouse gas (GHG) emissions from livestock production contribute 18% to total anthropogenic emissions. Emissions from beef and dairy represent three quarters of this figure, and beef production emissions have risen by an estimated 59% in the past half century in response to increasing global population and wealth. In line with international climate commitments, there exists pressure for beef production systems to increase the emissions efficiency of production, and life cycle assessment (LCA) studies have proved a powerful tool to this end. Application of this knowledge to policy is hampered by the heterogeneity of agricultural systems, however, and farm-level GHG accounting tools contribute a flexible, bottom-up solution to this. A variety of such tools are available, but a number of issues hinder their uptake. This thesis therefore set out to a) identify the most important issues affecting the efficacy and uptake of extant farm-level GHG accounting tools and b) develop a farm-level model (AgRE Calc) to address these issues. A review and test of existing tools found that differences in scope and methodology cause substantial differences in results calculated from common input datasets, an issue exacerbated by the methodological opacity of many tools. The empirical test conducted here provides insight into this, and also highlights the need for such tools to maintain simplicity in input data requirements, whilst maximising flexibility and detail in the output. To this end, the impact of cattle ration composition on modelled emissions was identified as a key parameter. The AgRE Calc model was developed to improve this aspect of the methodology, and used to carbon footprint data from a lifetime experiment focusing on beef finishing strategies and diets. Results of this study suggested that high quality grass-based diets have the potential to be as efficient as housed finishes. Additionally, the importance of good-quality, low-granularity activity data to the precision of the footprint was identified, as was the potential for variability in performance within treatments. The study also highlighted the pivotal role of grazing quality in emissions intensity of production. Literature review found that practitioners and models typically broadly estimate this parameter; this approach lacks accuracy and flexibility, so a novel methodology was defined to enable empirical estimation of this variable. Utilising simplistic input data already required by AgRE Calc, a regression model was developed to predict grazed forage digestibility in relation to sward age and nitrogen fertilisation levels. The model predicts decreasing digestibility, resulting in lower performance and higher enteric emissions, as swards age and fertilisation levels decrease. Monte Carlo simulation was also used to provide an estimate of the uncertainty surrounding this variable, and the results suggest that manipulation of pasture digestibility could be a useful mitigation strategy for emissions from extensive beef production. Uncertainty in modelled emissions was a common thread in these studies, and this was explored in more detail. AgRE Calc was developed for a Monte Carlo-based assessment of epistemic uncertainty within farm-level models. The resulting study found that uncertainty in N2O and purchased feed emission factors was the greatest source of farm-level emissions uncertainty. These factors greatly reduce the certainty with which comparisons between intensive and extensive approaches can be made. As such, it is recommended that uncertainty assessment in future form a greater aspect of farm-level and LCA assessments for livestock, and the methods and data compiled as part of this thesis form a basis for accomplishing this through Monte Carlo simulation. Together, these assessments provide a framework for the development of farm-level tools with a view to increasing their usability and relevance. A number of areas in which further progress can be made are identified, and the thesis argues for recognition of the niche filled by farm-level approaches by the developers of GHG accounting methodologies. As such, the thesis as a whole provides a thorough blueprint for advancement of farm-level modelling of GHG emissions, alongside a comprehensive synthesis of the state of the art.

Meat and milk from sub Saharan African cattle systems tend to have high greenhouse gas emissions intensities; this is largely due to low levels of productivity. There is a need to increase production to meet an increasing demand for livestock commodities; driven by growing populations, and growing diet variation, as incomes and urbanisation increase. Without measures to reduce the emissions intensity of production, there will be significant increases in total greenhouse gas emissions. Therefore, cost-effective ways of reducing emissions intensity, whilst increasing productivity should be identified. This thesis looks to support this by providing an assessment of low-input to semi-intensified cattle production systems in Senegal, West Africa; where cattle populations are growing and efforts are being made to increase domestic milk production. The emissions intensity of protein from current production systems is calculated using a version of the Food and Agriculture Organization’s Global Livestock Environmental Assessment Model (GLEAM). Variation in emissions intensity is observed between current systems, which can be largely linked to feed ration quality and levels of protein productivity. Productivity improving interventions suitable for the study systems are identified, and their application to current systems modelled by altering input parameters within GLEAM. It is suggested that production systems could reduce emissions intensities by applying nutritional and health related intervention packages; through which the varying production systems could abate between 10% and 20% of their total greenhouse gas emissions whilst also making financial savings. A comparison between the current systems of production also suggests that changing the lower productivity systems to match higher producing systems would also offer substantial cost-saving emissions abatement. The thesis considers the key limitation to the use of GLEAM for modelling the application of nutritional mitigation measures, in that when nutritional improvements are made animal performance does not currently increase. Predicting how animals will respond to improved nutrition is challenging. However, a methodology is discussed, and is shown to have an important effect on the emissions abatement results. Subsequently, the thesis advocates further research to experimentally substantiate animal performance responses when nutritionally limited cattle are given improved feed regimes. Despite the study livestock keepers showing aspiration to improve the productivity of their herds, with subsequent potential to reduce greenhouse gas emissions, the thesis recognises that the abatement potentials suggested by modelling would be restricted by the reality of production system context and constraints. Key barriers to a realisation of the productivity improvements include: a lack of financial means, limitations to resource access and affordability, and requirement for information and training concerning productivity improving options. For realisation of productivity improvements the current barriers would require further investigation, the thesis helps identify what form interventions should take.

This study aims to evaluate Rural Development Policy (RDP) and its ability to meet environmental objectives at European, national and regional levels. Policy evaluation is necessary to assess the processes and impact of policies and programmes to meet desired outcomes, to further ensure accountability of public funds. There a number of evaluation approaches that have varying abilities to examine the variety of interacting policy determinants. This study explores both quantitative and qualitative methods to evaluate the Scottish RDP Rural Priorities scheme performance for the programme period 2007-2013. The Rural Priorities scheme is an important mechanism for achieving environmental objectives through regionalisation. Spatial econometrics, in-depth interviews, and stakeholder power mapping were used to assess policy determinants, such as; farming characteristics, land capability, designated sites status, and accessibility and population as well as less tangible policy aspects such as; policy design, stakeholder power balances, and governance structures. Furthermore these methods were assessed singularly and in collaboration in their abilities to identify strengths and weakness in RDP participatory and environmental performance. This diversity of information contributes to the European Commission funded research project, SPARD (Spatial Analysis of Rural Development), which aims to help policy-makers understand the causal relationships between rural development measures and their consequences in a spatial dimension.

Hill farming systems in Scotland are the result of long evolution and adaptation to financial, social and political changes. Farming in the hills is a major contributor to rural industry and plays an important role in the economy, environment and social cohesion of these areas. However, it is fragile and has been dependent for many decades on high and continued levels of support payments. Agricultural land managers in these hill areas are also under increasing pressure from the other land use groups whose interests lie outside farming. With recent agricultural reforms, shifts in policy orientations regarding land use and changes in support, the future role of hill farming remains uncertain. This thesis sets out to examine the role of hill farming in this context of change, by investigating how hill farmers respond to changing policy, by understanding what other interested stakeholders expect from the hills, and exploring how hill farmers may have to adapt their farming system in response to these changes and expectations. Using an adaptive conjoint analysis method, stakeholders’ expectations have been assessed. Multivariate analysis and participative research with hill farmers have also been carried out, to typify their management responses to policy changes, using the 2003 CAP reform as an example. Stakeholders’ expectations and farmers’ types were then used in a linear programming optimisation model, to explore how hill farmers can maximise their financial margins under different policy and market change scenarios, and how their motivation is a drive towards adaptation. The results suggest that whilst livestock production is identified by stakeholders as one of the most important features for the hill areas, the continuity of livestock farming in the hills is threatened, as hill farmers are strongly affected by policy and market changes. This research also shows that there is a strong diversity in hill farming systems and in hill farmers’ management styles and motivations. That, perhaps, is one of the most important factors to acknowledge when formulating policies. This research also highlights the vulnerability of hill farming businesses (especially hill cattle production) to market price volatility, policy, subsidies and support changes, making it difficult for any hill farmer to withstand these fluctuations. Although different land uses, such as planting forestry, potentially bring substantial economic benefits, they are dependent upon many other restricting factors, including government grants, and require long-term commitment before benefits are seen. Integration of different land uses and productions could be one economic option for these areas, but a set of measures, perhaps including livestock-linked subsidies that recognise the diversity of hill farmers is needed, as well as options for hill farmers to be able to provide other public goods. Unless there is stability in market prices, a policy drive towards increasing efficiency, adequate subsidies and support and more readily attractive diversification options, including the provision of other ecosystem services linked to these grassland systems, this thesis suggests that there is a danger of hill land abandonment, a further decrease in hill farming activity, leading eventually to a decline in rural areas, not unlike many other marginal hill and mountain areas in the UK and Europe.

Agriculture has been reported to contribute a significant amount of greenhouse gases to the atmosphere among other anthropogenic activities. With still more than 870 million people in the world suffering from under-nutrition and a growing global food demand, it is relevant to study ways for mitigating the environmental impact of food production. The objective of this work was to identify gaps in the knowledge regarding the main factors affecting greenhouse gas (GHG) emissions from beef farming systems, to reduce the uncertainty on carbon footprint predictions, and to study the relative importance of mitigation options at the system level. A lack of information in the literature was identified regarding the quantification of the relevant animal characteristics of extensive beef systems that can impact on methane (CH4) outputs. In a meta-analysis study, it was observed that the combination of physiological stage and type of diet improved the accuracy of CH4 emission rate predictions. Furthermore, when applied to a system analysis, improved equations to predict CH4 from ruminants under different physiological stages and diet types reduced the uncertainty of whole-farm enteric CH4 predictions by up to 7% over a year. In a modelling study, it was demonstrated that variations in grazing behaviour and grazing choice have a potentially large impact upon CH4 emissions, which are not normally mentioned within carbon budget calculations at either local or national scale. Methane estimations were highly sensitive to changes in quality of the diet, highlighting the importance of considering animal selectivity on carbon budgets of heterogeneous grasslands. Part of the difficulties on collecting reliable information from grazing cattle is due to some limitations of available techniques to perform CH4 emission measurements. Thus, the potential use of a Laser Methane Detector (LMD) for remote sensing of CH4 emissions from ruminants was evaluated. A data analysis method was developed for the LMD outputs. The use of a novel technique to assess CH4 production from ruminants showed very good correlations with independent measurements in respiration chambers. Moreover, the use of this highly sensitive technique demonstrates that there is more variability associated with the pattern of CH4 emissions which cannot be explained by the feed nutritional value. Lastly, previous findings were included in a deterministic model to simulate alternative management options applied to upland beef farming systems. The success of the suggested management technologies to mitigate GHG emissions depends on the characteristics of the farms and management previously adopted. Systems with high proportion of their land unsuitable for cropping but with an efficient use of land had low and more certain GHG emissions, high human-edible returns, and small opportunities to further reduce their carbon footprint per unit of product without affecting food production, potential biodiversity conservation and the livelihood of the region. Altogether, this work helps to reduce the uncertainty of GHG predictions from beef farming systems and highlights the essential role of studies with a holistic approach to issues related to climate change that encompass the analysis of a large range of situations and management alternatives.

This Thesis explored a range of approaches to study the uncertainty and impacts associated with climate change at the farm scale in Scotland. The research objective was to use a process of uncertainty evaluation and simulation modelling to provide evidence of how primary production components of agriculture in Scotland may change under a future climate. The work used a generic Integrated Modelling Framework to structure the following sequence of investigations: Evaluate a Regional Climate Model‟s hindcast estimates (1960-1990) against observed weather data; Develop bias correction „downscaling factors‟ to be applied to the Regional Climate Model‟s future estimates; Evaluate the impacts of weather data sources (observed and modelled) on estimates made by a cropping systems model (CropSyst); Estimate values for a range of agro-meteorological metrics using observed and estimated downscaled future weather data; Simulate spring barley and winter wheat growth using CropSyst with observed and modelled weather data; Develop CropSyst in order to represent grass growth, evaluate estimates against a set of a priori criteria and determine suitability for use in a whole farm model. Conduct counter-factual assessments of the impacts of climate change and potential adaptation options using a whole farm model (LADSS). The study aimed to use tools on a spectrum of land use modelling complexity: agro-meteorological metrics (simple), CropSyst (intermediate), and the whole-farm integrated model (complex). Such an approach had a path dependency, in that to use the livestock system model component within the whole farm model, CropSyst had to make estimates of an acceptable quality for grass production. CropSyst however failed to meet the a priori evaluation criteria. This, coupled with technical and time constraints in running LADSS, led to the decision not to run the whole farm model. The findings were organised within the concepts of resilience and adaptive capacity. Results gained showed that the HadRM3 Regional Climate Model was capable of making both good and poor estimates of weather variables in the UK, and that downscaling improved the match between hindcast and observed weather data significantly. A sensitivity analysis involving introducing uncertainty from weather data sources within CropSyst showed that care was needed in interpreting estimates of future crop production. The agro-meteorological metrics indicated that whilst growing season length increases, the date of end of field capacity does not. The projected changes in crop production will likely be more positive if crop responses to elevated CO2 are considered. However, there will be additional constraints on crop growth due to increases in duration and magnitude of periods of growth limiting soil water deficits. Without adaptation to crop varieties with slower phenological development, yield decreases are seen in spring barley and winter wheat. The thesis concludes, whilst recognising the caveats and limitations of the methods used and the multiple range of external influencing issues, that the biophysical impacts at the farm scale in Scotland are within the boundaries of resilience, given that achievable adaptation options exist and are undertaken. The dynamics of farm scale management will need to adjust to cope with higher levels of water stress, but opportunities will also arise for greater flexibility in land use mixes. Crop yield can increase due to more favourable growing conditions and cultivar adaptations. These conclusions, when placed within the context of climate change impacts and adaptive cycles at a global scale, indicate that agriculture in Scotland has the potential to cope with the impacts but that substantial changes are required in farming practices.