A dynamic simulation model for powdery mildew epidemics on winter wheat*

A system dynamic model for epidemics of Blumeria graminis (powdery mildew) on wheat was elaborated, based on the interaction between stages of the disease cycle, weather conditions and host characteristics. The model simulates the progress of disease severity, expressed as a percentage of powdered leaf area, on individual leaves, with a time step of one day, as a result of two processes: the growth of fungal colonies already present on the leaves and the appearance of new colonies. By means of mathematical equations, air temperature, vapour pressure deficit, rainfall and wind are used to calculate incubation, latency and sporulation periods, the growth of pathogen colonies, infection and spore survival. Effects of host susceptibility to infection, and of leaf position within the plant canopy, are also included. Model validation was carried out by comparing model outputs with the dynamics of epidemics observed on winter wheat grown at several locations in northern Italy (1991–98). Simulations were performed using meteorological data measured in standard meteorological stations. As there was good agreement between model outputs and actual disease severity, the model can be considered a satisfactory simulator of the effect of environmental conditions on the progress of powdery mildew epidemics.     

A model estimating the risk of Fusarium head blight on wheat*

A dynamic simulation model for the risk of Fusarium head blight on wheat was elaborated based on systems analysis. The model calculates a daily infection risk based on sporulation, spore dispersal and infection of host tissue of the four main species causing the disease (Gibberella zeae, Fusarium culmorum, Gibberella avenacea, Monographella nivalis). Spore yield and dispersal are calculated as functions of temperature, rainfall and relative humidity, while the main factors affecting the infection rate are temperature, wetness and the host growth stage. The model also calculates a risk for mycotoxin production by G. zeae and F. culmorum in the infected head tissue. First validations against field data, collected in some wheat‐growing areas in northern Italy and not used in model elaboration, produced satisfactory results.     

Estimating the potential development of Diaporthe helianthi epidemics in Italy*

Diaporthe helianthi is the causal agent of a severe sunflower disease but, in Italy, disease outbreaks are sporadic with no significant losses. The present work investigates the role of meteorological conditions on the potential development of D. helianthi epidemics in Italy, using the French model Asphodel, which simulates the effect of air temperature, relative humidity and rainfall on ascospore maturation and dispersal, infection establishment, disease onset and severity during the period of host susceptibility. Meteorological data measured in eight stations distributed from north to south Italy, over a 5‐year period (1995–99), was used as model input. Results showed that meteorological conditions in Italy are frequently favourable for D. helianthi infections on sunflower, and severe epidemics are possible. Therefore, climatic conditions are not a limiting factor for disease development in the Italian sunflower‐growing areas. The lack of disease epidemics in Italy may be related to differences in the pathogen populations compared with the French ones.     

Expression of the KIT protein (CD117) in primary cutaneous mast cell tumors of the dog.

Thirty-one canine cutaneous masses, diagnosed as mast cell tumors (MCT) by histopathologic analysis, were used to evaluate the immunohistochemical pattern of expression of KIT protein (CD117), a type III tyrosine kinase protein involved in mast cell growth and differentiation. Lesions were graded as I (well differentiated), II (intermediate differentiation), or III (poorly differentiated) according to the following morphologic features: invasiveness, cellularity and cellular morphology, mitotic index, and stromal reaction. Immunohistochemical KIT expression was compared with histologic grade and some histomorphologic features (cell differentiation and nuclear grade) evaluated separately. A possible predictive role of biologic behavior in MCTs for KIT expression was also investigated. Immunohistochemical analysis revealed three different patterns of KIT expression: a cytoplasmic diffuse pattern, a membranous pattern with immunostaining located on the cell surface, and a cytoplasmic perinuclear pattern, where KIT expression was detected in the cytoplasm of the neoplastic mast cells, close to the nucleus. Statistical analysis showed a close relationship between different KIT immunohistochemical patterns and histologic grade (P < 0.00000), cell differentiation (P < 0.00000), and nuclear grade (P < 0.0024). According to Kaplan-Meier-estimated survival curves compared by survival analysis, KIT expression was significantly associated with survival time (P = 0.037) but not cancer-free interval (P = 0.50). Similar to other well-known histomorphological features, KIT expression is a useful parameter of malignancy in cutaneous MCTs. KIT expression also predicted the biological behavior of the tumors in this study.     

Firm Demography and Regional Development: Evidence from Italy

This article contributes to the literature on firm demography and regional development in at least three different ways. First, consumption, rather than employment, which is the most common variable seen in literature, is used to measure the impact of firm demography on regional development. Second, while the literature is mainly focused on the relationship between new business formation and regional development, we investigate both entry and exit flows of firms. Third, we decompose each of these flows into spatial and sectoral components. The empirical investigation looks at the Italian regions with reference to the period 2004–2009. Results seem to be substantially divergent between the South and the rest of Italy.     

Genomic prediction for yields,processing and nutritional quality traits in cultivated potato (Solanum tuberosum L.)

Current potato breeding approaches are hampered by several factors including costly seed tubers, tetrasomic inheritance and inbreeding depression. Genomic selection (GS) demonstrated interesting results regardless of the ploidy level, and can be harnessed to circumvent these problems. In this work, three GS models were evaluated using 50,107 informative SilicoDArT markers and 11 traits in two values for cultivation and use (VCU) potato trials. Two key breeding problems modelled included predicting the performance of (i) new and unphenotyped clones (cross‐validation) and (ii) a VCU using another as training set (TS). GS models performed comparably. Cross‐validation accuracy was high for D35, D45, DMW and BVAL, in ascending order. Prediction accuracies of the VCUs were highly correlated, but the best prediction was obtained for the smaller VCU using the bigger as TS. Cross‐validation and VCU prediction accuracies were higher when bigger TSs were used. The findings herein indicate that GS can be attractively integrated in potato breeding, particularly in early clonal generations to predict and select for traits with low heritability which would otherwise require more testing years, environments and resources.