One approach to the application of site-specific techniques and technologies in precision agriculture is to subdivide a field into a few contiguous homogenous zones, often referred to as management zones (MZs). Delineating MZs can be based on some sort of clustering, however there is no widely accepted method. The application of fuzzy set theory to clustering has enabled researchers to account better for the continuous variation in natural phenomena. Moreover, the methods based on non-parametric density estimation can detect clusters of unequal size and dispersion. The objectives of this paper were to:(1) compare different procedures for creating management zones and (2) determine the relation of the MZs delineated with potential yield. One hundred georeferenced point measurements of soil and crop properties were obtained from a 12 ha field cropped with durum wheat for two seasons. The trial was carried out at the experimental farm of CRA-CER in Foggia (Italy). All variables were interpolated on a 1 9 1 m grid using the geostatistical techniques of kriging and cokriging. The techniques compared to identify MZs were: (1) the ISODATA method, (2) the fuzzy c-means algorithm and (3) a nonparametric density algorithm. The ISODATA method, which was the simplest, subdivided the field into three distinct classes of suitable size for uniform management, whereas the other two methods created two classes. The non-parametric density algorithm characterized the edge properties between adjacent clusters more efficiently than the fuzzy method. The clusters from the non-parametric density algorithm and yield maps for three seasons (were compared and agreement measures were computed. The kappa coefficients for the three seasons were negative or small positive values which indicate only slight agreement. These results illustrate the importance of temporal variation in spatial variation of yield in rainfed conditions, which limits the use of the MZ approach.

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The monitoring of burned areas can easily be performed using satellite multispectral images: several indices are available in the literature for highlighting the differences between healthy vegetation areas and burned areas, in consideration of their different signatures. However, these indices may have limitations determined, for example, by the presence of clouds or water bodies that produce false alarms. To avoid these inaccuracies and optimize the results, this work proposes a new index for detecting burned areas named Normalized Burn Ratio Plus (NBR+), based on the involvement of Sentinel-2 bands. The efficiency of this index is verified by comparing it with five other existing indices, all applied on an area with a surface of about 500 km2 and covering the north-eastern part of Sicily (Italy). To achieve this aim, both a uni-temporal approach (single date image) and a bi-temporal approach (two date images) are adopted. The maximum likelihood classifier (MLC) is applied to each resulting index map to define the threshold separating burned pixels from non-burned ones. To evaluate the efficiency of the indices, confusion matrices are constructed and compared with each other. The NBR+ shows excellent results, especially because it excludes a large part of the areas incorrectly classified as burned by other indices, despite being clouds or water bodies.

Agro-ecozoning is a delineation of landscape into relatively homogeneous regions of expected similar crop performance. Past classifications have been subjective, crop specific and did not take into account spatial correlation. A quantitative approach is proposed to unambiguously locate, characterise and visualise agro-ecozones and their boundaries which can be allied to different environmental conditions. In this study the environmental parameters, including climatic and soil characteristics, hypothesized to be generally relevant to many crops in Capitanata-Foggia (South Italy), were used. Cokriged environmental estimates at 500 m scale were used in a clustering algorithm based on non-parametric multivariate density estimation. A 3D map of density estimation and red-green-blue colour triplet were used for visualisation of agro-ecozones as a unique combination of environmental factors. The proposed approach produced the delineation of the study area in five compact classes in the space of environmental attributes that were also contiguous in geographic space. The resulting agro-ecozones may provide a framework for useful application in land use decision making.

An efficient management of water resources is considered very important for Mediterranean regions of Italy in order to improve the economical and environmental sustainability of the agricultural activity. The purpose of this study is to analyze the components of soil water balance in an important district included in the regions of Basilicata and Puglia and situated in the Jonical coastal area of Southern Italy and mainly cropped with horticultural crops. The study was performed by using the spatially distributed and physically based model SIMODIS in order to individuate the best irrigation management maximizing the water use efficiency and minimizing water losses by deep percolation and soil evaporation. SIMODIS was applied taking in to account the soil spatial variability and localization of cadastral units for two crops, durum wheat and water melon. For water melon recognition in 2007 a remote sensed image, from SPOT5 satellite, at the spatial resolution of 10 m, has been used. In 2008, a multi-temporal data set was available, from SPOT5 satellite to produce a land cover map for the classes water melon and durum wheat. Water melon cultivation was simulated adopting different water supply managements: rainfed and four irrigation strategies based on (i) soil water availability and (ii) plant water status adopting a threshold daily stress value. For each management, several water management indicators were calculated and mapped in GIS environment. For seasonal irrigation depth, actual evapotranspiration and irrigation efficiency were also determined. The analysis allowed to individuate the areas particularly sensitive to water losses by deep percolation because of their hydraulic functions characterized by low water retention and large values of saturated hydraulic conductivity. For these areas, the irrigation based on plant water status caused very high water losses by drainage. On the contrary, the irrigation scheduled on soil base allowed to control better this component of soil water balance. SIMODIS resulted a useful tool to analyse the soil water balance at spatial scale and to support the local irrigation authority for planning the irrigation water distribution