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The co-occurrence of moniliformin (MON), fumonisins (FBs), and deoxynivalenol (DON) was evaluated in maize, durum, and common wheat grown in different experimental fields located in several Italian regions. MON was quantified using a LC-MS/MS method adding lanthanum ions in the mobile phase. In maize, MON contamination was widespread and considerable; the toxin was detected in almost all the samples (95.1%) and exceeded 500 and 1000 µg kg−1 in 42.0% and in 18.5% of samples, respectively. Significant positive correlation was found between MON and FB contamination levels. When there were not droughty climate conditions, a positive significant correlation was found between growing degree days (GDD) and MON values. In wheat, MON contamination was not widespread like in maize and it was lower in common wheat than in durum wheat. In durum wheat, MON was detected in 45.0% of the samples with only 6 samples (7.5%) exceeding 500 µg kg−1, while in common wheat the toxin was detected above the LOD in 18.7% of samples exceeding 100 µg kg−1 in only two samples (2.5%). No correlation was found with DON contamination. Climate conditions influenced both MON and DON occurrence. [ABSTRACT FROM AUTHOR]

The expected increase in population and the pressure posed by climate change on agricultural production require the assessment of future yield levels and the evaluation of the most suitable management options to minimize climate risk and promote sustainable agricultural production. Crop simulation models are widely applied tools to predict crop development and production under different management practices and environmental conditions. The aim of this study was to parameterize CSM-CERES-Wheat and CSM-CERES-Maize models, implemented in the Decision Support System for Agrotechnology Transfer (DSSAT) software, to predict phenology and grain yield of durum wheat, common wheat, and maize in different Italian environments. A 10-year (2001–2010) dataset was used to optimize the genetic parameters for selected varieties of each species and to evaluate the models considering several statistical indexes. The generalized likelihood uncertainty estimation method, and trial and error approach were used to optimize the cultivar-specific parameters of these models. Results show good model performances in reproducing crop phenology and yield for the analyzed crops, especially with the parameters optimized with the trial and error procedure. Highly significant (p ≤ 0.001) correlations between observed and simulated data were found for both anthesis and yield in model calibration and evaluation (p ≤ 0.01 for grain yield of maize in model evaluation). Root mean square error (RMSE) values range from six to nine days for anthesis and from 1.1 to 1.7 t ha−1 for crop yield and index of agreement (d-index) from 0.96 to 0.98 for anthesis and from 0.8 to 0.87 for crop yield. The set of genetic parameters obtained for durum wheat, common wheat, and maize may be applied in further analyses at field, regional, and national scales to guide operational (farmers), strategic, and tactical (policy makers) decisions. [ABSTRACT FROM AUTHOR]