Agricultural decision support systems (DSS) may be argued to have passed sequentially through phases of unbelief, euphoria and disappointment and to be passing into either a phase of maturity with realistic expectations of the technology or to abandonment. This paper proposes that agricultural DSS in their widest sense still have a significant role to play in shaping land use and management to meet society's changing requirements. The paper draws its conclusions from the experiences of a team developing farming-systems models and from market research into the commercial potential of such models as DSS.
This paper explores how deliberative workshops might enhance social learning about climate change adaptation among land managers in northwest Europe (Scotland). To date, methods for enhancing social learning in the context of adaptation and climate change have been neglected. In this study, location specifi c agro-meteorological indicators for both observed and future climate data were produced. The indicators were used as a basis for discussion in four deliberative workshops. The workshops sought to raise awareness of climate change issues, ensure the validity and utility of the indicators, stimulate thinking about adaptive responses and increase land managers' capacity to adapt. Land managers' adaptations to climate change fell into four broad categories: changing what they do, how they do it, when they do it or the frequency with which they do it. This paper therefore refl ects on the use of deliberative workshops as an effective technique to enhance social learning regarding adapting to climate change.Social learning's normative goal, to improve the management of human and environmental interrelations, makes it appropriate to address challenges of climate change and land management. Agriculture and related systems remain the principal land-use sector by area for much of the world (Matthews et al., 2008d) and agriculture remains a key policy priority due to employment in the agri-food supply chain, security of food supply and environmental stewardship (Adger, 2001). Climate change is identifi ed as a key threat to rural communities in Scotland's Sustainable Development and Climate Change Strategies (Scottish Executive 2005. Scotland has responded to this challenge via its Climate Change (Scotland) Act 2009. Climate change scenarios suggest the need for adaptation by land managers, as changes in weather will have considerable impact on management practices and yields. Land managers need to understand the nature of these changes and respond, drawing on a combination of local and the best available scientifi c knowledge.This paper describes how social learning was stimulated through deliberation over climate change trends and indicators. The paper begins by reviewing current work on climate change adaptation in agriculture before highlighting how this could be enriched by drawing on the social learning literature. The case study design and the methods used are described, before the fi ndings are discussed in terms of potential adaptation strategies and the role of workshops in enhancing social learning. The implications of these fi ndings in terms of policy are considered before the paper is concluded.
This paper argues that an integrated assessment approach, combining simulation modelling with deliberative processes involving decision makers and other stakeholders, has the potential to generate credible and relevant assessments of climate change impacts on farming-systems. The justification for the approach proposed is that while simulation modelling provides an effective way of exploring the range of possible impacts of climate change and a means of testing the consequences of possible management or policy interventions, the interpretation of the outputs is highly dependent on the point of view of the stakeholder. Inevitably, whatever the responses to climate change, there will be trade-offs between the benefits and costs to a range of stakeholders. The use of a deliberative process that includes stakeholders, both in defining the topics addressed and in debating the interpretations of the outcomes, addresses many of the limitations that have been previously identified in the use of computer-based tools for agricultural decision support. The paper further argues that the concepts of resilience and adaptive capacity are useful for the assessment of climate change impacts as they provide an underpinning theory for processes of change in land use systems. The integrated modelling framework (IMF) developed for the simulation of whole-farm systems is detailed, including components for crop and soil processes, livestock systems and a tool for scheduling of resource use within management plans. The use of the IMF for assessing climate change impacts is then outlined to demonstrate the range of analyses possible. The paper concludes with a critique of the IA approach and notes that issues of quantification and communication of uncertainty are central to the success of the methodology.
This paper compares precipitation, maximum and minimum air temperature and solar radiation estimates from the Hadley Centre's HadRM3 regional climate model (RCM), (50×50 km grid cells), with observed data from 15 meteorological station in the UK, for the period 1960-90. The aim was to investigate how well the HadRM3 is able to represent weather characteristics for a historical period (hindcast) for which validation data exist. The rationale was to determine if the HadRM3 data contain systematic errors and to investigate how suitable the data are for use in climate change impact studies at particular locations. Comparing modelled and observed data helps assess and quantify the uncertainty introduced to climate impact studies. The results show that the model performs very well for some locations and weather variable combinations, but poorly for others. Maximum temperature estimations are generally good, but minimum temperature is overestimated and extreme cold events are not represented well. For precipitation, the model produces too many small events leading to a serious under estimation of the number of dry days (zero precipitation), whilst also over-or underestimating the mean annual total. Estimates represent well the temporal distribution of precipitation events. The model systematically over-estimates solar radiation, but does produce good quality estimates at some locations. It is concluded that the HadRM3 data are unsuitable for detailed (i.e. daily time step simulation model based) site-specific impacts studies in their current form. However, the close similarity between modelled and observed data for the historical case raises the potential for using simple adjustment methods and applying these to future projection data.
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