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SALVATORE LA BELLA

Simulation of the effects of climate change on barley yields in rural Italy

  • Autori: Tuttolomondo, T; La Bella, S; Lecardane, G; Leto, C
  • Anno di pubblicazione: 2009
  • Tipologia: eedings
  • Parole Chiave: Simulation model, climate variability, barley yields in rural areas
  • OA Link: http://hdl.handle.net/10447/46307

Abstract

The Greenhouse effect is considered to be one of the most influential factors on climate change today, especially where temperature and rainfall levels/distribution are concerned, making it of considerable importance in the field of Agronomy. Crop growth and development simulation models are a valuable cognitive tool in understanding water and nutrient dynamics in soil/plant systems. This paper looks at the direct and indirect effects of climatic changes on average barley yields. The complex nature of the study rendered the use of mathematical simulation models essential, both for predicting future climate conditions and for the simulation of crop growth and development. Of the different simulation models currently employed in the agricultural sector, the DSSAT (Decision Support System for Agrotechnology Transfer) model was used with the help of ENEA - Ente Nazionale per le Nuove Tecnologie, l’Energia e l’Ambiente (National Centre for New Technologies, Energy and the Environment) in Rome. Initial research included the calibration and validation of the CERES – Barley growth model with agronomic experimental data taken from areas belonging to the national network of the Experimental Institute for Cereal Cultivation, each from four macro land areas in Italy (S. Angelo Lodigiano (MI) for the North of Italy; Jesi (AN) for Central Italy; Foggia (FG) for the South of Italy; Cammarata (AG) for the Islands). The calibrated and validated model was used to simulate crop yields for 99 years under increased interannual climate variability conditions. The implementation of adequate monitoring systems, with advanced data management and the development of models such as those used in this study, is crucial to sector policy-making and in choosing methods for effect mitigation. Furthermore, the application of prediction models is essential in determining correct agronomic practices which are profitable, eco-compatible and long-lasting.