No abstract

The potential of mathematical models is widely acknowledged for examining components and interactions of natural systems, estimating the changes and uncertainties on outcomes, and fostering communication between scientists with different backgrounds and between scientists, managers and the community. For favourable reception of models, a systematic accrual of a good knowledge base is crucial for both science and decision-making. As the roles of models grow in importance, there is an increase in the need for appropriate methods with which to test their quality and performance. For biophysical models, the heterogeneity of data and the range of factors influencing usefulness of their outputs often make it difficult for full analysis and assessment. As a result, modelling studies in the domain of natural sciences often lack elements of good modelling practice related to model validation, that is correspondence of models to its intended purpose. Here we review validation issues and methods currently available for assessing the quality of biophysical models. The review covers issues of validation purpose, the robustness of model results, data quality, model prediction and model complexity. The importance of assessing input data quality and interpretation of phenomena is also addressed. Details are then provided on the range of measures commonly used for validation. Requirements for a methodology for assessment during the entire model-cycle are synthesised. Examples are used from a variety of modelling studies which mainly include agronomic modelling, e.g. crop growth and development, climatic modelling, e.g. climate scenarios, and hydrological modelling, e.g. soil hydrology, but the principles are essentially applicable to any area. It is shown that conducting detailed validation requires multi-faceted knowledge, and poses substantial scientific and technical challenges. Special emphasis is placed on using combined multiple statistics to expand our horizons in validation whilst also tailoring the validation requirements to the specific objectives of the application.

No abstract

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.

No abstract

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.

No abstract

No abstract