Abstract: Crop growth simulation models are increasingly used for regionally assessing the effects of climate change and variability on crop yields. These models require spatially and temporally detailed, location-specific, environmental (weather and soil) and management data as inputs, which are often difficult to obtain consistently for larger regions. Aggregating the resolution of input data for crop model applications may increase the uncertainty of simulations to an extent that is not well understood. The present study aims to systematically analyse the effect of changes in the spatial resolution of weather input data on yields simulated by four crop models (LINTUL-SLIM, DSSAT-CSM, EPIC and WOFOST) which were utilized to test possible interactions between weather input data resolution and specific modelling approaches representing different degrees of complexity. The models were applied to simulate grain yield of spring barley in Finland for 12 years between 1994 and 2005 considering five spatial resolutions of daily weather data: weather station (point) and grid-based interpolated data at resolutions of 10 km x 10 km; 20 km x 20 km; 50 km x 50 km and 100 km x 100 km. Our results show that the differences between models were larger than the effect of the chosen spatial resolution of weather data for the considered years and region. When displaying model results graphically, each model exhibits a characteristic ‘fingerprint’ of simulated yield frequency distributions. These characteristic distributions in response to the inter-annual weather variability were independent of the spatial resolution of weather input data. Using one model (LINTUL-SLIM), we analysed how the aggregation strategy, i.e. aggregating model input versus model output data, influences the simulated yield frequency distribution. Results show that aggregating weather data has a smaller effect on the yield distribution than aggregating simulated yields which causes a deformation of the model fingerprint. We conclude that changes in the spatial resolution of weather input data introduce less uncertainty to the simulations than the use of different crop models but that more evaluation is required for other regions with a higher spatial heterogeneity in weather conditions, and for other input data related to soil and crop management to substantiate our findings. Our results provide further evidence to support other studies stressing the importance of using not just one, but different crop models in climate assessment studies. (C) 2013 Elsevier B.V. All rights reserved.

Abstract: The impact of climate change on European agriculture is subject to a significant uncertainty, which reflects the intertwined nature of agriculture. This issue involves a large number of processes, ranging from field to global scales, which have not been fully integrated yet. In this study, we intend to help bridging this gap by quantifying the effect of farm-scale autonomous adaptations in response to changes in climate. To do so, we use a modelling framework coupling the STICS generic crop model to the AROPAj microeconomic model of European agricultural supply. This study provides a first estimate of the role of such adaptations, consistent at the European scale while detailed across European regions. Farm-scale autonomous adaptations significantly alter the impact of climate change over Europe, by widely alleviating negative impacts on crop yields and gross margins. They significantly increase European production levels. However, they also have an important and heterogeneous impact on irrigation water withdrawals, which exacerbate the differences in ambient atmospheric carbon dioxide concentrations among climate change scenarios. (c) 2012 Elsevier B.V. All rights reserved.

Abstract: In order to feed the world we need innovative thinking on how to increase agricultural production whilst also mitigating climate change. Agriculture and land-use change are responsible for approximately one-third of total anthropogenic greenhouse gas (GHG) emissions but hold potential for climate change mitigation but are only tangentially included in UNFCCC mitigation policies. To get a full estimate of GHG emissions from agricultural crop production both energy-based emissions and land-based emissions need to be accounted for. Furthermore, the major mitigation potential is likely to be indirect reduction of emissions i.e. reducing emissions per unit of agricultural product rather than the absolute emissions per se. Hence the system productivity must be included in the same analysis. This paper presents the Kaya-Porter identity, derived from the Maya identity, as a new way to calculate GHG emissions from agricultural crop production by deconstructing emissions into five elements; the GHG intensity of the energy used for production (kg CO2-eq./MJ), energy intensity of the production (MJ/kg dry matter), areal productivity (kg dry matter/ha), areal land-based GHG emissions (CO2-eq./ha) and area (ha). These separate elements in the identity can be targeted in emissions reduction and mitigation policies and are useful to analyse past and current trends in emissions and to explore future scenarios. Using the Kaya-Porter identity we have performed a case study on Danish crop production and find emissions to have been reduced by 12% from 1992 to 2008, whilst yields per unit area have remained constant. Both land-based emissions and energy-based emissions have decreased, mainly due to a 41% reduction in nitrogen fertilizer use. The initial identity based analysis for crop production presented here needs to be extended to include livestock to reflect the entire agricultural production and food demand sectors, thereby permitting analysis of the trade-offs between animal and plant food production, human dietary preferences and population and resulting GHG emissions. (C) 2012 Elsevier B.V. All rights reserved.