Daniel L. Sandars

Environmental Benefits of Livestock Manure Management Practices and Technology by Life Cycle Assessment

Abstract An environmental Life Cycle Assessment (LCA) procedure is constructed to compare the total emissions from different techniques for managing livestock wastes. Life Cycle Assessment is a method of holistically and systematically accounting for the environmental benefits and burdens of the production of goods and services including consequential burdens generated elsewhere. As waste emissions are very variable, the methodology is extended to include the uncertainty in the estimates in order to indicate the significance of differences between techniques. The object is to inform policy of whether options are better for the environment by quantifying potential emissions abatement, by highlighting priority environmental impacts and by revealing compromises for further investigation. This paper reports comparative LCAs for several pig waste management options. For example, various slurry application techniques, including: splash plates, band spreaders and injection. If the splash-plate system is taken as a reference, the injector system causes only 64% of the environmental acidification and 71% of the eutrophication of surface waters. The benefits must be offset against the increase in nitrate leaching of 50%. In contrast, the band spreader system offers 28% of the benefits of injection. The environmental impacts have also been expressed as a proportion of the UK national emissions. This gives each impact a weighted-value that enables direct comparisons of disparate impacts. Although band spreader systems showed an aggregated, or total, environmental impact reduction of almost 10%, the reduction is not significant when uncertainty is taken into account. Using an anaerobic digester shows few overall benefits due to the fugitive losses of methane. However, if these can be eliminated the global warming potential from waste management is reduced close to zero.

A perspective on operational research prospects for agriculture

This paper discusses the future of operational research (OR) for the agricultural industries in a broad sense, including horticulture and viticulture during a period of increased pressure on natural resources. The authors use their experience in the field along with published literature, to draw insights into new opportunities for OR, and how the OR community might adapt to realise these opportunities best. Trends in demand for food security and biofuels, the quest for sustainability, information technology (IT), and commercial power create new opportunities to support strategic investment and operations management within both primary production and the related supply chains. To realise such potential, the agricultural OR community needs to improve management of stakeholder relations, interdisciplinary synthesis, and the successful application of OR.

An assessment of ways to abate ammonia emissions from UK livestock buildings and waste stores. Part 1: ranking exercise

Abstract The options for abating ammonia emissions from livestock buildings and waste stores under UK conditions have been assessed. There is increasing interest in reducing such emissions, which contribute to long-range atmospheric pollution problems, and which, via subsequent deposition, can also harm sensitive ecosystems. A literature review was first carried out, and a “brainstorm” idea-generating session was held, together leading to lists of feasible abatement approaches: one for each of a range of types of livestock buildings and waste stores. A ranking exercise was then conducted. A set of aspects of each application of each feasible abatement approach was drawn up: the aspects considered included not only capital costs and running costs but also such others as animal welfare, stage of development and knock-on effects. Each aspect of each application of each approach was given a ranking of between 1 (very poor) and 5 (very good). When the aspects of “potential abatement” and “achievable abatement now”, as well as either “capital costs” or “animal welfare”, were weighted by a factor of 5, the “best bet” abatement approaches for livestock buildings were identified as (a) dietary manipulation (for all types), (b) raising the C/N ratio by generous use of bedding (for any buildings based on solid manure), (c) exhaust air cleaning, especially by bioscrubbers (for force-ventilated pig and poultry buildings), (d) oil layers or (e) fixing with acid (for slurry-based piggeries) and (f) drying by ventilation of muck (for any poultry building). For waste stores, the “best bet” abatement approaches were identified as (g) replacing storage with industrial scale processing or (h) with land filling (niche solutions only), (i) dietary manipulation, (j) fixing with acid (slurries only), (k) increasing the surfaces resistance to ammonia volatilisation (by crusts for cattle slurry, but by floating granules for pig slurry), (l) minimising surface area by heap shape (solid manures), and (m) cover sheets (solid manures). For stored poultry manure, the low moisture content allowed good showings by the additional options of (n) drying, and, in the case of poultry manure with litter in certain parts of the UK, (p) sale for combustion in a Non-Fossil Fuel Obligation (NOFFO) power station, the electricity from which commands a higher price than that from a conventional power station. A closer look at the economic consequences of different abatement approaches was taken by means of a mathematical model: this work is reported in a companion paper.

Interactively modelling land profitability to estimate European agricultural and forest land use under future scenarios of climate, socio-economics and adaptation

Studies of climate change impacts on agricultural land use generally consider sets of climates combined with fixed socio-economic scenarios, making it impossible to compare the impact of specific factors within these scenario sets. Analysis of the impact of specific scenario factors is extremely difficult due to prohibitively long run-times of the complex models. This study produces and combines metamodels of crop and forest yields and farm profit, derived from previously developed very complex models, to enable prediction of European land use under any set of climate and socio-economic data. Land use is predicted based on the profitability of the alternatives on every soil within every 10’ grid across the EU. A clustering procedure reduces 23,871 grids with 20+ soils per grid to 6,714 clusters of common soil and climate. Combined these reduce runtime 100 thousand-fold. Profit thresholds define land as intensive agriculture (arable or grassland), extensive agriculture or managed forest, or finally unmanaged forest or abandoned land. The demand for food as a function of population, imports, food preferences and bioenergy, is a production constraint, as is irrigation water available. An iteration adjusts prices to meet these constraints. A range of measures are derived at 10’ grid-level such as diversity as well as overall EU production. There are many ways to utilise this ability to do rapid What-If analysis of both impact and adaptations. The paper illustrates using two of the 5 different GCMs (CSMK3, HADGEM with contrasting precipitation and temperature) and two of the 4 different socio-economic scenarios (“We are the world”, “Should I stay or should I go” which have contrasting demands for land), exploring these using two of the 13 scenario parameters (crop breeding for yield and population) . In the first scenario, population can be increased by a large amount showing that food security is far from vulnerable. In the second scenario increasing crop yield shows that it improves the food security problem.

Can we be certain about future land use change in Europe? A multi-scenario, integrated-assessment analysis

The global land system is facing unprecedented pressures from growing human populations and climatic change. Understanding the effects these pressures may have is necessary to designing land management strategies that ensure food security, ecosystem service provision and successful climate mitigation and adaptation. However, the number of complex, interacting effects involved makes any complete understanding very difficult to achieve. Nevertheless, the recent development of integrated modelling frameworks allows for the exploration of the co-development of human and natural systems under scenarios of global change, potentially illuminating the main drivers and processes in future land system change. Here, we use one such integrated modelling framework (the CLIMSAVE Integrated Assessment Platform) to investigate the range of projected outcomes in the European land system across climatic and socio-economic scenarios for the 2050s. We find substantial consistency in locations and types of change even under the most divergent conditions, with results suggesting that climate change alone will lead to a contraction in the agricultural and forest area within Europe, particularly in southern Europe. This is partly offset by the introduction of socioeconomic changes that change both the demand for agricultural production, through changing food demand and net imports, and the efficiency of agricultural production. Simulated extensification and abandonment in the Mediterranean region is driven by future decreases in the relative profitability of the agricultural sector in southern Europe, owing to decreased productivity as a consequence of increased heat and drought stress and reduced irrigation water availability. The very low likelihood (< 33% probability) that current land use proportions in many parts of Europe will remain unchanged suggests that future policy should seek to promote and support the multifunctional role of agriculture and forests in different European regions, rather than focusing on increased productivity as a route to agricultural and forestry viability.

A review of the practice and achievements from 50 years of applying OR to agricultural systems in Britain

This paper will survey how things have changed over nearly 50 years of operational research (OR) applied to agriculture. The first ‘OR group’ was set up at the National Institute of Agricultural Engineering by Dan Boyce in 1969 and is now at Cranfield University. It will examine how, and what, factors have influenced the type of work and the methods used. What applications have stood the test of time and what are just distant memories in paper publications? It will show that agricultural OR has moved on from its early beginnings in agriculture in applying OR techniques with simple analyses, to using and creating complex computer models. While it might be described as alive, it clearly needs to identify itself and its specific contribution to analysing decisions, to set it apart from the ‘anyone can simulate and optimize using a computer’. The skill of holistic systems modelling of combinations of processes at the decision-maker level is as important as the ability to use techniques.

Key challenges and priorities for modelling European grasslands under climate change.

Grassland-based ruminant production systems are integral to sustainable food production in Europe, converting plant materials indigestible to humans into nutritious food, while providing a range of environmental and cultural benefits. Climate change poses significant challenges for such systems, their productivity and the wider benefits they supply. In this context, grassland models have an important role in predicting and understanding the impacts of climate change on grassland systems, and assessing the efficacy of potential adaptation and mitigation strategies. In order to identify the key challenges for European grassland modelling under climate change, modellers and researchers from across Europe were consulted via workshop and questionnaire. Participants identified fifteen challenges and considered the current state of modelling and priorities for future research in relation to each. A review of literature was undertaken to corroborate and enrich the information provided during the horizon scanning activities. Challenges were in four categories relating to: 1) the direct and indirect effects of climate change on the sward 2) climate change effects on grassland systems outputs 3) mediation of climate change impacts by site, system and management and 4) cross-cutting methodological issues. While research priorities differed between challenges, an underlying theme was the need for accessible, shared inventories of models, approaches and data, as a resource for stakeholders and to stimulate new research. Developing grassland models to effectively support efforts to tackle climate change impacts, while increasing productivity and enhancing ecosystem services, will require engagement with stakeholders and policy-makers, as well as modellers and experimental researchers across many disciplines. The challenges and priorities identified are intended to be a resource 1) for grassland modellers and experimental researchers, to stimulate the development of new research directions and collaborative opportunities, and 2) for policy-makers involved in shaping the research agenda for European grassland modelling under climate change.

Empirical Test of an Agricultural Landscape Model: The Importance of Farmer Preference for Risk Aversion and Crop Complexity

Developing models to predict the effects of social and economic change on agricultural landscapes is an important challenge. Model development often involves making decisions about which aspects of the system require detailed description and which are reasonably insensitive to the assumptions. However, important components of the system are often left out because parameter estimates are unavailable. In particular, measurements of the relative influence of different objectives, such as risk, environmental management, on farmer decision making, have proven difficult to quantify. We describe a model that can make predictions of land use on the basis of profit alone or with the inclusion of explicit additional objectives. Importantly, our model is specifically designed to use parameter estimates for additional objectives obtained via farmer interviews. By statistically comparing the outputs of this model with a large farm-level land-use data set, we show that cropping patterns in the United Kingdom contain a significant contribution from farmer’s preference for objectives other than profit. In particular, we found that risk aversion had an effect on the accuracy of model predictions, whereas preference for a particular number of crops grown was less important. While nonprofit objectives have frequently been identified as factors in farmers’ decision making, our results take this analysis further by demonstrating the relationship between these preferences and actual cropping patterns.

How do farm models compare when estimating greenhouse gas emissions from dairy cattle production

The European Union Effort Sharing Regulation (ESR) will require a 30% reduction in greenhouse gas (GHG) emissions by 2030 compared with 2005 from the sectors not included in the European Emissions Trading Scheme, including agriculture. This will require the estimation of current and future emissions from agriculture, including dairy cattle production systems. Using a farm-scale model as part of a Tier 3 method for farm to national scales provides a more holistic and informative approach than IPCC (2006) Tier 2 but requires independent quality control. Comparing the results of using models to simulate a range of scenarios that explore an appropriate range of biophysical and management situations can support this process by providing a framework for placing model results in context. To assess the variation between models and the process of understanding differences, estimates of GHG emissions from four farm-scale models (DairyWise, FarmAC, HolosNor and SFARMMOD) were calculated for eight dairy farming scenarios within a factorial design consisting of two climates (cool/dry and warm/wet)×two soil types (sandy and clayey)×two feeding systems (grass only and grass/maize). The milk yield per cow, follower:cow ratio, manure management system, nitrogen (N) fertilisation and land area were standardised for all scenarios in order to associate the differences in the results with the model structure and function. Potential yield and application of available N in fertiliser and manure were specified separately for grass and maize. Significant differences between models were found in GHG emissions at the farm-scale and for most contributory sources, although there was no difference in the ranking of source magnitudes. The farm-scale GHG emissions, averaged over the four models, was 10.6 t carbon dioxide equivalents (CO2e)/ha per year, with a range of 1.9 t CO2e/ha per year. Even though key production characteristics were specified in the scenarios, there were still significant differences between models in the annual milk production per ha and the amounts of N fertiliser and concentrate feed imported. This was because the models differed in their description of biophysical responses and feedback mechanisms, and in the extent to which management functions were internalised. We conclude that comparing the results of different farm-scale models when applied to a range of scenarios would build confidence in their use in achieving ESR targets, justifying further investment in the development of a wider range of scenarios and software tools.

Determining sectoral and regional sensitivity to climate and socio-economic change in Europe using impact response surfaces

Responses to future changes in climatic and socio-economic conditions can be expected to vary between sectors and regions, reflecting differential sensitivity to these highly uncertain factors. A sensitivity analysis was conducted using a suite of impact models (for health, agriculture, biodiversity, land use, floods and forestry) across Europe with respect to changes in key climate and socio-economic variables. Depending on the indicators, aggregated grid or indicative site results are reported for eight rectangular sub-regions that together span Europe from northern Finland to southern Spain and from western Ireland to the Baltic States and eastern Mediterranean, each plotted as scenario-neutral impact response surfaces (IRSs). These depict the modelled behaviour of an impact variable in response to changes in two key explanatory variables. To our knowledge, this is the first time the IRS approach has been applied to changes in socio-economic drivers and over such large regions. The British Isles region showed the smallest sensitivity to both temperature and precipitation, whereas Central Europe showed the strongest responses to temperature and Eastern Europe to precipitation. Across the regions, sensitivity to temperature was lowest for the two indicators of river discharge and highest for Norway spruce productivity. Sensitivity to precipitation was lowest for intensive agricultural land use, maize and potato yields and Scots pine productivity, and highest for Norway spruce productivity. Under future climate projections, North-eastern Europe showed increases in yields of all crops and productivity of all tree species, whereas Central and East Europe showed declines. River discharge indicators and forest productivity (except Holm oak) were projected to decline over southern European regions. Responses were more sensitive to socio-economic than to climate drivers for some impact indicators, as demonstrated for heat-related mortality, coastal flooding and land use.