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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. [ABSTRACT FROM AUTHOR]

The Cameroonian agricultural sector, a critical part of the local ecosystem, is potentially vulnerable to climate change, thus raising concerns about food security in the country''s future. Adaptations policies may be able to mitigate some of this vulnerability. This article addresses the issue of selected adaptation options within the context of Cameroonian food production. A methodology is applied where transient diagnostics of two atmosphere-ocean general circulation models, the NASA/Goddard Institute GISS and the British HadCM3, are coupled to a cropping system simulation model (CropSyst). This methodology simulates current and future (2020, 2080) crop yields for selected key crops such as bambara nut, groundnut, maize, sorghum, and soybean, in eight agricultural regions of Cameroon. Our results show that for the future, substantial yield increases are estimated for bambara groundnut, soybean and groundnut, while little or no change or even decreases for maize and sorghum yields, varying according to the climate scenario and the agricultural region investigated. Taking the ?no regrets? principle into consideration, we also explore the advantages of specific adaptation strategies specifically for three crops, maize, sorghum and bambara groundnut, under GISS A2 and B2 marker scenarios only. Here, changing sowing dates may be ineffective in counteracting adverse climatic effects because of the narrow rainfall band that strictly determines the timing of farm operations in Cameroon. In contrast, the possibility of developing later maturing new cultivars proved to be very effective in offsetting adverse impacts, giving the highest increases in productivity under different scenario projections without management changes. For example, under climate change scenario GISS A2 2080, a 14.6% reduction in maize yield was converted to a 32.1% increase; a 39.9% decrease in sorghum yield was converted to a 17.6% increase, and for bambara groundnut, yields were almost trebled due to increase length of growing period and the positive effects of higher CO2concentrations. These results better inform wider studies and development strategies on sustainable agriculture in the area by providing an indication as to the potential direction in shifts in production capabilities. Our approach highlights the benefit of using models as tools to investigate potential climate change impacts, where results can supplement existing knowledge. The findings also provide useful guidance and motivation to public authorities and development agencies interested in food security issues in Cameroon and elsewhere. [ABSTRACT FROM AUTHOR]