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Empirical modelling of regional and national durum wheat quality

Empirical modelling of regional and national durum wheat quality

Toscano, P., Genesio, L., Crisci, A., Vaccari, F.P., Ferrari, E., Cava, P. La, Porter, J.R. and Gioli, B. (2015) Empirical modelling of regional and national durum wheat quality. Agricultural and Forest Meteorology, 204. pp. 67-78. ISSN 0168-1923 (doi:

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The production of durum wheat in the Mediterranean basin is expected to experience increased variability in yield and quality as a consequence of climate change. To assess how environmental variables and agronomic practices affect grain protein content (GPC), a novel approach based on monthly gridded input data has been implemented to develop empirical model, and validated on historical time series to assess its capability to reproduce observed spatial and inter-annual GPC variability. The model was applied in four Italian regions and at the whole national scale and proved reliable and usable for operational purposes also in a forecast ‘real-time’ mode before harvesting. Precipitable water during autumn to winter and air temperature from anthesis to harvest were extremely important influences on GPC; these and additional variables, included in a linear model, were able to account for 95% of the variability in GPC that has occurred in the last 15 years in Italy. Our results are a unique example of the use of modelling as a predictive real-time platform and are a useful tool to understand better and forecast the impacts of future climate change projections on durum wheat production and quality.

Item Type: Article
Additional Information: [1] Acknowledgements (funding): The authors acknowledge Barilla S.p.A. for providing financial support for Delphi2 project. This paper contributes to the FACCE-JPI MACSUR project ‘Modelling European Agriculture with Climate Change for Food Security’ and the ADAPTAWHEAT FP7 project (contract no: FP7-KBBE-2011-5/289842).
Uncontrolled Keywords: Durum wheat; grain protein content, forecasting tool, modelling, gridded data
Subjects: S Agriculture > SB Plant culture
Faculty / School / Research Centre / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > Natural Resources Institute
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Last Modified: 07 Apr 2017 20:45

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