Abstract: In this study, the performance of nine widely used and accessible crop growth simulation models (APES-ACE, CROPSYST, DAISY, DSSAT-CERES, FASSET, HERMES, MONICA, STICS and WOFOST) was compared during 44 growing seasons of spring barley (Hordeum vulgare L) at seven sites in Northern and Central Europe. The aims of this model comparison were to examine how different process-based crop models perform at multiple sites across Europe when applied with minimal information for model calibration of spring barley at field scale, whether individual models perform better than the multi-model mean, and what the uncertainty ranges are in simulated grain yields. The reasons for differences among the models and how results for barley compare to winter wheat are discussed. Regarding yield estimation, best performing based on the root mean square error (RMSE) were models HERMES, MONICA and WOFOST with lowest values of 1124, 1282 and 1325 (kg ha(-1)), respectively. Applying the index of agreement (IA), models WOFOST, DAISY and HERMES scored best having highest values (0.632, 0.631 and 0.585, respectively). Most models systematically underestimated yields, whereby CROPSYST showed the highest deviation as indicated by the mean bias error (MBE) (-1159 kg ha(-1)). While the wide range of simulated yields across all sites and years shows the high uncertainties in model estimates with only restricted calibration, mean predictions from the nine models agreed well with observations. Results of this paper also show that models that were more accurate in predicting phenology were not necessarily the ones better estimating grain yields. Total above-ground biomass estimates often did not follow the patterns of grain yield estimates and, thus, harvest indices were also different. Estimates of soil moisture dynamics varied greatly. In comparison, even though the growing cycle for winter wheat is several months longer than for spring barley, using RMSE and IA as indicators, models performed slightly, but not significantly, better in predicting wheat yields. Errors in reproducing crop phenology were similar, which in conjunction with the shorter growth cycle of barley has higher effects on accuracy in yield prediction. (C) 2012 Elsevier B.V. All rights reserved.

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: Urea has become the predominant source of synthetic nitrogen (N) fertilizer used throughout the world. Among the various available mitigation tools, urease inhibitors like NBPT have the most potential to improve efficiency of urea by reducing N losses, mainly via ammonia volatilization. However, there is a lack of information on the effect of N-(n-butyl) thiophosphoric triamide (NBPT) on other N losses such as gaseous emissions of N2O and NO and NO3− leaching. A two-year field experiment using irrigated maize (Zea mays) crop was carried out under Mediterranean conditions to evaluate the effectiveness of urea coated with NBPT (0.4%, w/w) alone and with both NBPT and nitrification inhibitor dicyandiamide (DCD) (0.4 and 3%, w/w, respectively) to mitigate N2O–N, NO–N and NO3−–N losses. The different treatments of U, U+NBPT and U+NBPT+DCD were applied to the maize crop in 2009 and then in 2010. The 2010 maize crop followed a fallow period, during which the 2009 crop residues were incorporated into the soil. Two different irrigation regimes were followed each year. In 2009, irrigation was controlled for the first 2 weeks following urea fertilization; whereas, the 2010 crop period was characterized by increased irrigation in the same period. After each treatment application, measurements of the changes in soil mineral N, gaseous emissions of N2O and NO, nitrate leaching and biomass production were made. N2O emissions were effectively abated by NBPT and NBPT+DCD and were reduced by 54 and 24%, respectively, in 2009. A reduction in nitrification rate by the inhibitors was also observed during 2009. In 2010 cropping period, NBPT reduced N2O emissions by 4%; while the combination of NBPT and DCD treatment reduced N2O emission by 43%. Yield-scaled N2O emissions were reduced by 50 and 18% by NBPT and the mixture of NBPT+DCD, respectively, in 2009. Applying inhibitors did not have any significant effect on yield-scaled N2O emissions in the 2010 crop period. Total NO losses from urea were 2.25 kg NO–N ha−1 in the 2009 crop period and 5 times lower in the following year; this may provide an indicator of the prevalence of nitrification as the main process in the production of N2O in the 2009 maize crop. Most of the NO3− was lost within the fallow period (i.e. 92, 81 and 75% of the total NO3− leached for U, U+NBPT and U+NBPT+DCD, respectively), so the incorporation of crop residues was not as effective as expected at reducing these N losses. Our study suggests that the effectiveness of NBPT and combination of NBPT+DCD in reducing N losses from applied urea is influenced by management practices, such as irrigation, and climatic conditions.