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The research reports an analysis on satellite vegetation indices (VIs) derived from Landsat 5, 7 and 8 imagery at various crop stages compared to yield monitoring data, to estimate the final yield. Five years georeferenced grain harvest data were archived in a 11.07 ha experimental field located in a flat area in Northern Italy. Multispectral images were downloaded, and various Vis were computed: simple ratio (SR), normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), enhanced vegetation index (EVI), green normalized difference vegetation index (GNDVI), and green chlorophyll index (GCI). Final GY (t/ha) and selected VIs were compared using geostatistics (ArcGIS version 10.3) by computing the empirical semivariograms and interpolating with the spherical model. Dates during crop growth showing highest correlations between VIs and GY were recognized as the best times/crop stages for final GY prediction. Then, geostatistics was applied to the best VIs to assess the correspondence with GY at same pixel level. Three correspondence levels were set, and final agreement was determined based on their comprehensive assessment in all pixels. This study demonstrated a final agreement between VIs and GY in the range 64-86%. Final agreement was shown to be adversely related to its CV, meaning that more consistent correspondence levels were conductive to higher overall agreement, and vice versa. Landsat satellite imagery proved a good potential for estimating final GY over different crops in a rotation, at a relatively small field scale (11.07 ha)

Digital humanitarians represent the current generation of volunteers providing timely contributions in the form of digital map data in the aftermath of natural disasters. Starting from the tragic 2010 earthquake in Haiti and thanks to the success of the OpenStreetMap (OSM) project, the presence and coordination of these volunteers have grown incredibly over the past years. This work investigates the dynamics of the mapping process and the nature of the OSM volunteers who contributed map data after the 2016 earthquake in Central Italy. The analyses show that the existing OSM users were the majority of those contributing to the mapping activity, with less edits performed by new users. The collaborative mapping process was efficiently coordinated through a dedicated platform and the area hit by the earthquake was significantly edited in OSM after the disaster.

A key task in process mining consists of building a graph of causal dependencies over process activities, which can then help derive more expressive models in some high-level modeling language. An approach to accomplishing this task is presented, where the learning process can exploit background knowledge available to the analyst. The method is based on encoding the information gathered from the log and the (possibly) given background knowledge in terms of precedence constraints, i.e., constraints over the topology of the graphs. Learning algorithms are formulated in terms of reasoning problems over precedence constraints. The whole approach has been implemented in a prototype system and results of experimental activity are reported.

Availability of georeferenced yield data involving different crops over years, and their use in future crop management, are a subject of growing debate. In a 9 hectare field in Northern Italy, seven years of yield data, including wheat (3 years), maize for biomass (2 years), sunflower, and sorghum, and comprising remote (Landsat) normalized difference vegetation index (NDVI) data during central crop stages, and soil analysis (grid sampling), were subjected to geostatistical analysis (semi-variogram fitting), spatial mapping (simple kriging), and Pearson’s correlation of interpolated data at the same resolution (30 m) as actual NDVI values. Management Zone Analyst software indicated two management zones as the optimum zone number in multiple (7 years) standardized yield data. Three soil traits (clay content, total limestone, total nitrogen) and five dates within the NDVI dataset (acquired in different years) were shown to be best correlated with multiple- and single-year yield data, respectively. These eight parameters were normalized and combined into a two-zone multiple soil and NDVI map to be compared with the two-zone multiple yield map. This resulted in 83% pixel agreement in the high and low zone (89 and 10 respective pixels in the soil and NDVI map; 73 and 26 respective pixels in the yield map) between the two maps. The good agreement, which is due to data buffering across different years and crop types, is a good premise for differential management of the soil- and NDVI-based two zones in future cropping seasons.

ABTRACT: Soil and weather data were used to analyse spatio-temporal yield patterns of winter cereals (wheat) and spring dicots (sunflower and coriander) in an 11-ha field in Northern Italy (44.5° N, 12.2° E), during 2010–2014. Three yield stability classes (YSCs) were established over multiple years, based on spatio-temporal characteristics across the field: high yielding and stable (HYS), low yielding and stable (LYS), and unstable. The HYS class (46% of field area) staged a 122% relative yield with low temporal variability. The unstable class (24% of field area) was slightly more productive (83% relative yield) than the LYS class (30% of field area, and 80% relative yield), but less consistent over time. Crop yields evidenced negative correlations with sand content; positive correlations with silt and clay content. Soil properties were quite consistently classified among YSCs: the LYS and unstable classes were associated with higher sand content and lower cation exchange capacity, suggesting that these characteristics lead to fluctuation and depression of final yield. Establishing YSCs based on spatio-temporal yield appears a sound approach to appraise field potential. It results in strategic and tactical decisions to be taken, depending on the profile of spatial and temporal productivity in different field areas.