Integration of meteo-climatic and remote sensing information for the analysis of durum wheat quality in Val d’Orcia (Tuscany, Italy)

Climatic and weather conditions largely affect agricultural activities, modifying plant responses and determining the quantity and quality of products. In this respect, the aim of this research was to analyze the quality of winter durum wheat (Triticum turgidum L. var. durum) in terms of protein content (%) and test weight through the combined use of meteorological information from ground stations and remote sensing. Meteorological conditions were described using both temperature and precipitation from ground weather stations and the North Atlantic Oscillation (NAO) index. The spectral index used for monitoring the crop was the Normalized Difference Vegetation Index (NDVI). The analysis was carried out for the period 1999-2009 in Tuscany, Central Italy. Air temperature of the period from February to June was positively correlated with grain protein concentration, while test weight showed negative correlations to temperature in March and May and for all multi-monthly periods starting from March onward. Grain protein concentration appeared as negatively correlated with precipitation cumulated during the entire period from November to June, while no significant effect was observed in test weight. With regard to the NAO, higher correlations to protein content were found for the monthly values of November and February, but more significant results were obtained by aggregating NAO index on a multi-monthly basis when November and February were included. On the other hand, the correlations were negative between the winter NAO index and the specific weight mainly in February and for the period from February to March. Finally, NDVI starting from mid May showed to be negatively related to protein content and positively to test weight. These results demonstrated that precipitation and air temperature over the production area represent two crucial variables affecting growth and development of winter durum wheat. On the other hand, the use of large-scale meteorological information and specific spectral indices showed a great possibility in the perspective of a local quality forecast system setup.