Abstract: Many climate change studies have been carried out in different parts of the world to assess climate change vulnerability and adaptation capacity of agricultural crops for certain environments characterized from climatic, pedological and agronomical point of view. The objective of this study was to analyse the productive response of winter durum wheat and tomato to climate change and sowing/transplanting time in one of the most productive areas of Italy (i.e. Capitanata, Puglia), using CERES-Wheat and CROPGRO cropping system models. Three climatic datasets were used: i) a single dataset (50 km x 50 km) provided by the JRC European centre for the period 1975- 2005; two datasets from HadCM3 for the IPCC A2 GHG scenario for time slices with +2°C (centred over 2030-2060) and +5°C (centred over 2070-2099), respectively. All three datasets were used to generate synthetic climate series using a weather simulator (model LARS-WG). No negative yield effects of climate change were observed for winter durum wheat with delayed sowing (from 330 to 345 DOY) increasing the average dry matter grain yield under forecasted scenarios. Instead, the warmer temperatures were primarily shown to accelerate the phenology, resulting in decreased yield for tomato under the + 5°C future climate scenario. In general, under global temperature increase by 5°C, early transplanting times could minimize the negative impact of climate change on crop productivity but the intensity of this effect was not sufficient to restore the current production levels of tomato cultivated in southern Italy.

Abstract: The accurate estimation of crop grain nitrogen (N; N in grain yield) is crucial for optimizing agricultural N management, especially in crop rotations. In the present study, 12 process-based models were applied to simulate the grain N of i) seven crops in rotations, ii) across various pedo-climatic and agro-management conditions in Europe, under both continuous simulation and single year simulation, and for iv) two calibration levels, namely minimal and detailed calibration. Generally, the results showed that the accuracy of the simulations in predicting grain N increased under detailed calibration. The models performed better in predicting the grain N of winter wheat (Triticum aestivum L.), winter barley (Hordewn vulgare L.) and spring barley (Hordeum vulgare L.) compared to spring oat (Avena saliva L.), winter rye (Secale cereale L.), pea (Piswn sativum L.) and winter oilseed rape (Brassica napus L.). These differences are linked to the intensity of parameterization with better parameterized crops showing lower prediction errors. The model performance was influenced by N fertilization and irrigation treatments, and a majority of the predictions were more accurate under low N and rainfed treatments. Moreover, the multi-model mean provided better predictions of grain N compared to any individual model. In regard to the Individual models, DAISY, FASSET, HERMES, MONICA and STICS are suitable for predicting grain N of the main crops in typical European crop rotations, which all performed well in both continuous simulation and single year simulation. Our results show that both the model initialization and the cover crop effects in crop rotations should be considered in order to achieve good performance of continuous simulation. Furthermore, the choice of either continuous simulation or single year simulation should be guided by the simulation objectives (e.g. grain yield, grain N content or N dynamics), the crop sequence (inclusion of legumes) and treatments (rate and type of N fertilizer) included in crop rotations and the model formalism.