Sustainability of Italian Agriculture: A Methodological Approach for Assessing Crop Water Footprint at Local Scale

Thursday, 18 December 2014
Filiberto Altobelli1, Anna Dalla Marta2, Orlando Cimino1, Simone Orlandini2 and Francesca Natali2, (1)National Institute of Agricutural Economics, INEA, Rome, Italy, (2)University of Florence, Department of Agrifood Production and Environmental Sciences, DISPAA, Florence, Italy
In a world where population is rapidly growing and where several planetary boundaries (i.e. climate change, biodiversity loss and nitrogen cycle) have already been crossed, agriculture is called to respond to the needs of food security through a sustainable use of natural resources.

In particular, water is one of the main elements of fertility so the agricultural activity, and the whole agro-food chain, is one of the productive sectors more dependent on water resource and it is able to affect, at regional level, its availability for all the other sectors.

In this study, we proposed a methodology for assessing the green and blue water footprint of the main Italian crops typical of the different geographical areas (northwest, northeast, center, and south) based on data extracted from Italian Farm Accountancy Data Network (FADN). FADN is an instrument for evaluating the income of agricultural holdings and the impacts of the Common Agricultural Policy.

Crops were selected based on incidence of cultivated area on the total arable land of FADN farms net. Among others, the database contains data on irrigation management (irrigated surface, length of irrigation season, volumes of water, etc.), and crop production. Meteorological data series were obtained by a combination of local weather stations and ECAD E-obs spatialized database.

Crop water footprints were evaluated against water availability and risk of desertification maps of Italy. Further, we compared the crop water footprints obtained with our methodology with already existing data from similar studies in order to highlight the effects of spatial scale and level of detail of available data.