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Abstract: Sensitivity analysis studies how the variation in model outputs can be due to different sources of variation. This issue is addressed, in this study, as an application of sensitivity analysis techniques to a crop model in the Mediterranean region. In particular, an application of Morris and Sobol'' sensitivity analysis methods to the rice model WARM is presented. The output considered is aboveground biomass at maturity, simulated at five rice districts of different countries (France, Greece, Italy, Portugal, and Spain) for years characterized by low, intermediate, and high continentality. The total effect index of Sobol'' (that accounts for the total contribution to the output variation due a given parameter) and two Morris indices (mean μ and standard deviation σ of the ratios output changes/parameter variations) were used as sensitivity metrics. Radiation use efficiency (RUE), optimum temperature (T opt), and leaf area index at emergence (LAIini) ranked in most of the combinations site×year as first, second and third most relevant parameters. Exceptions were observed, depending on the sensitivity method (e.g. LAIini resulted not relevant by the Morris method), or site-continentality pattern (e.g. with intermediate continentality in Spain, LAIini and T opt were second and third ranked; with low continentality in Portugal, RUE was outranked by T opt). Low σ values associated with the most relevant parameters indicated limited parameter interactions. The importance of sensitivity analyses by exploring site×climate combinations is discussed as pre-requisite to evaluate either novel crop-modelling approaches or the application of known modelling solutions to conditions not explored previously. The need of developing tools for sensitivity analysis within the modelling environment is also emphasized. [Copyright &y& Elsevier]

A crop simulation model must first be capable of representing the actual performance of crops grown in any region before it can be applied to the prediction of climate variability and change impacts. A cropping systems model (CropSyst) simulations of crop productivity in the sub-Saharan Central African (using Cameroon as the case study) region, under the current climate were compared with observed yields of maize, sorghum, groundnut, bambara groundnut and soybean from eight sites. The model produced both over-and-under estimates, but with a mean percentage difference of only –2.8%, ranging from –0.6% to –4.5%. Based on these results, we judged the CropSyst simulations sufficiently reliable to justify use of the model in assessing crop growth vulnerability to climatic changes in Cameroon and else where. [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]

In the original article, the panels of Figure 5 were also shown in Figure 6. The figure caption was correct. The original article has been corrected. [ABSTRACT FROM AUTHOR]