Bioclimatic analysis in a region of southern Italy (Calabria)

In this study, an analysis of precipitation and temperature data has been performed over 67 series observed in a region of southern Italy (Calabria). At first, to detect possible trends in the time series, an analysis was performed with the Mann–Kendall non-parametric test applied at monthly and seasonal scale. An additional investigation, useful for checking the climate change effects on vegetation, has also been included analysing bioclimatic indicators. In particular, Emberger, Rivas-Martinez and De Martonne indices were calculated by using monthly temperature and precipitation data in the period 1916–2010. The spatial pattern of the indices has been evaluated and, in order to link the vegetation and the indices,different indices maps have been intersected with the land cover data, given by the Corine Land Cover map. Moreover, the temporal evolution of the indices and of the vegetation has been analysed. Results suggest that climate change may be responsible for the forest cover change, but, given also the good relationship between the various types of bioclimate and forest formations, human activities must be considered

Estimating within-field variation using a nonparametric density algorithm

The application of site-specific techniques and technologies in precision farming requires subdividing a field into a generally small number of contiguous homogeneous zones. The proposed algorithm of clustering is based on nonparametric density estimate, where a cluster is defined as a region surrounding a local maximum of the probability density function. Soil samples were collected in a 2-ha field of the experimental farm of the Agricultural Research Institute, located in Foggia (Southern Italy) and some of the most production-affecting soil properties were interpolated by using the geostatistical techniques of kriging and cokriging. The application of the clustering approach to the (co)kriged surface variables produced the subdivision of the field into five distinct classes. The proposed algorithm proves quite promising in identifying spatially contiguous zones, which are more homogeneous in soil properties than the whole-field. Its great advantage consists in giving an additional description of the residual variation within the class and such a piece of information is very useful in precision farming as a basis for the variable-rate application of agronomic inputs