Eric Audsley

Modelling the spatial distribution of agricultural land use at the regional scale

Agriculture is the most important land use in Europe in geographic terms and because of this it plays a central role in the quality of the wider environment. Whilst the spatial patterns of agricultural land use have changed considerably in recent times, further changes are likely as a result of the influences of policy reform, socio-economics and climate change. Understanding, therefore, how agricultural land use might respond to global environmental change drivers is a research question of considerable importance. The first step, however, in projecting potential future changes in agricultural land use is to be able to understand and represent in models both the socio-economic and physical processes that control current land use distributions. Thus, this paper presents an approach to modelling the spatial distribution of agricultural land use at the regional scale. The approach is based on the simulation of farm-scale decision making processes (based on optimisation) and the response of crops to their physical environment. Regional scale applications of the model are undertaken through the use of spatially-variable, geographic data sets (soils, climate and topography) combined with economic data. Examples of the application of the model are given for two regions of England: the north-west and east Anglia. These regions were selected to give examples of contrasting land use systems within the context of northern European agriculture. The model results are compared statistically with observed distributions of agricultural land use for the same regions in a quasi-validation exercise. The comparison suggests that the model is very good at representing land use that is aggregated at the regional level, and at representing general spatial trends in land use patterns. Some differences were observed, however, in land use densities between the modelled and observed data. The results suggest that the basic hypothesis of the model: that farmers are risk averse, profit maximisers, is a reasonable assumption for the regions studied. However, further study of decision making processes would be likely to improve our ability to model agricultural land use distributions. This includes, for example, the role of farmer attitudes to risk, differing views on future prices and profitability, and the effect of time lags in the decision process.

Multiple objective linear programming for environmental farm planning

We present a multiple objective linear programming model developed to consider a wide range of farming situations, which allows optimisation of profit or environmental outcome(s) or both. The modelling considers the problem of planning a farming system within a world where environmental considerations are increasing. The objective is to identify the best cropping and machinery options which are both profitable and result in improvements to the environment, depending upon the farm situation of market prices, potential crop yields, soil and weather characteristics. In particular, the model uses a flexible approach to choosing the machinery, timing of operations, crop rotations and levels of inputs. We show for a UK scenario, that large reductions in environmental impact can be achieved for reductions in farm profit which are insignificant relative to the annual variation due to yields and prices.

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 Two-Stage Stochastic Programming with Recourse Model for Determining Robust Planting Plans in Horticulture

A two-stage stochastic programming with recourse model for the problem of determining optimal planting plans for a vegetable crop is presented in this paper. Uncertainty caused by factors such as weather on yields is a major influence on many systems arising in horticulture. Traditional linear programming models are generally unsatisfactory in dealing with the uncertainty and produce solutions that are considered to involve an unacceptable level of risk. The first stage of the model relates to finding a planting plan which is common to all scenarios and the second stage is concerned with deriving a harvesting schedule for each scenario. Solutions are obtained for a range of risk aversion factors that not only result in greater expected profit compared to the corresponding deterministic model, but also are more robust.

An arable farm model to evaluate the commercial viability of new machines or techniques

Abstract A linear programming model of an arable farm has been developed for the use of researchers or engineers developing new machines and techniques. The model assesses, within a range of farm conditions, the economic and technical bounds within which a machine must operate if it is to be commercially viable. The model is also useful for looking at different management strategies for individual farms. The user requires no knowledge of linear programming to understanding the input and output. The input is a booklet containing instructions and examples. The output is a series of tables showing optimum cropping, machinery use and annual profit.

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.

Modelling the effects of cross-sectoral water allocation schemes in Europe

Future renewable water resources are likely to be insufficient to meet water demand for human use and minimum environmental flow requirements in many European regions. Hence, fair and equitable water allocation to different water use sectors and environmental needs is important for climate change adaptation in order to reduce negative effects on human well-being and aquatic ecosystems. We applied a system of coupled sectoral metamodels of water availability and water use in the domestic, manufacturing industry, electricity generation, and agricultural sectors to simulate the effects of generic water allocation schemes (WAS) at the European level. The relative performance of WAS in balancing adverse impacts on the water use sectors and aquatic ecosystems was analysed for an ensemble of 16 scenarios for the 2050s, which were built from the combination of four socio-economic scenarios, developed in the CLIMSAVE project, and four climate projections based on IPCC A1. The results indicate that significant physical water shortages may result from climate and socio-economic change in many regions of Europe, particularly in the Mediterranean. In the energy sector, average annual water demand can largely be met even in water allocation schemes that deprioritise the sector. However, prioritisation of agricultural water demand has significant adverse impacts on the domestic and manufacturing industry sectors. Cross-sectoral impacts were found to be lowest if at least one of the domestic and manufacturing sectors is assigned higher priority than agriculture. We conclude that adapting spatial patterns of water-intensive activities to renewable water availability across Europe, such as shifting irrigated agriculture to less water-stressed basins, could be an effective demand-side adaptation measure, and thus a candidate for support through EU policy.

Direct and indirect impacts of climate and socio-economic change in Europe: a sensitivity analysis for key land- and water-based sectors

Integrated cross-sectoral impact assessments facilitate a comprehensive understanding of interdependencies and potential synergies, conflicts, and trade-offs between sectors under changing conditions. This paper presents a sensitivity analysis of a European integrated assessment model, the CLIMSAVE integrated assessment platform (IAP). The IAP incorporates important cross-sectoral linkages between six key European land- and water-based sectors: agriculture, biodiversity, flooding, forests, urban, and water. Using the IAP, we investigate the direct and indirect implications of a wide range of climatic and socio-economic drivers to identify: (1) those sectors and regions most sensitive to future changes, (2) the mechanisms and directions of sensitivity (direct/indirect and positive/negative), (3) the form and magnitudes of sensitivity (linear/non-linear and strong/weak/insignificant), and (4) the relative importance of the key drivers across sectors and regions. The results are complex. Most sectors are either directly or indirectly sensitive to a large number of drivers (more than 18 out of 24 drivers considered). Over twelve of these drivers have indirect impacts on biodiversity, forests, land use diversity, and water, while only four drivers have indirect effects on flooding. In contrast, for the urban sector all the drivers are direct. Moreover, most of the driver–indicator relationships are non-linear, and hence there is the potential for ‘surprises’. This highlights the importance of considering cross-sectoral interactions in future impact assessments. Such systematic analysis provides improved information for decision-makers to formulate appropriate adaptation policies to maximise benefits and minimise unintended consequences.

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.

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.