Kuehneola uredinis (cane and leaf rust).

Kuehneola uredinis is a fungal pathogen that causes cane and leaf rust only in Rubus cultivars, or wild and ornamental blackberry species. It can be highly destructive, especially on susceptible blackberry and Rubus hybrid cultivars, but rarely occurs on red and black raspberry (Ellis et al., 1991). Although the disease is not systemic, severe infection can cause premature defoliation and a decrease in cane vigour, making plants more susceptible to winter conditions and resulting in economic loss (Converse, 1966). Dispersal of the pathogen occurs on blackberry and hybrid Rubus cultivars, and wild blackberries in production areas worldwide. The fungus overwinters either as aecial urediniospores or mycelium in the canes and wet conditions favour disease dispersal (Laundon and Rainbow, 1969). K. uredinis is not included in quarantine lists (USDA, 2017; EPPO, 2019). There is interest in K. uredinis as a biocontrol agent due to its destructive effect on some invasive wild blackberry species, e.g., Rubus penetrans (Gardner and Hodges, 1983).

Kuehneola uredinis (cane and leaf rust).

Kuehneola uredinis is a fungal pathogen that causes cane and leaf rust only in Rubus cultivars, or wild and ornamental blackberry species. It can be highly destructive, especially on susceptible blackberry and Rubus hybrid cultivars, but rarely occurs on red and black raspberry (Ellis et al., 1991). Although the disease is not systemic, severe infection can cause premature defoliation and a decrease in cane vigour, making plants more susceptible to winter conditions and resulting in economic loss (Converse, 1966). Dispersal of the pathogen occurs on blackberry and hybrid Rubus cultivars, and wild blackberries in production areas worldwide. The fungus overwinters either as aecial urediniospores or mycelium in the canes and wet conditions favour disease dispersal (Laundon and Rainbow, 1969). K. uredinis is not included in quarantine lists (USDA, 2017; EPPO, 2019). There is interest in K. uredinis as a biocontrol agent due to its destructive effect on some invasive wild blackberry species, e.g., Rubus penetrans (Gardner and Hodges, 1983).

Simulasi Sebaran Penyakit Blas Pada Tanaman Padi Menggunakan Model Spatial Multi Criteria Evaluation: Studi Kasus Kabupaten Karawang dan Purwakarta

Dispersal Simulation of Rice Blast Disease Using Spatial Multi Criteria Evalution Model: Case Study In District of Karawang and PurwakartaRice blast is caused by Pyricularia oryzae. The potential epidemic of this disease can be spatially simulated using the MCA (Multi Criteria Analysis) method based on geographical characteristics, cultivation practices, and eviromental condition. A software that can be used for MCA is SMCE (Spatial Multi Criteria Evaluation). This study was aimed to predict the spatial dispersal of blast disease using SMCE model, and identify the factors that supports the epidemic. The study was conducted in February - August 2018 in Karawang and Purwakarta District. The research methods include observing the severity of blast disease, cultivation practices and environmental conditions, and analyzing SMCE. The SMCE analysis uses rice crop maps from the Sistem Monitoring Pertanaman Padi (Simotandi), which consists of grouping factors, standardizing factors, and weighting factors. The SMCE results are a simulation map of blast disease dispersal which is then compiled with predictions of its severity. Accuracy of prediction results was evaluated by MAPE (Mean Absolute Percentage Error) based on observational data on actual disease severity. The prediction results for Karawang and Purwakarta showed means of accuration 78.16% and 73.95% respectively. In general, factors that have a strong influence on the development of blast disease include altitude, distance from source of the epidemic, history of disease in the fields, number of spores (inoculum) trapped, irrigation quality, application of herbicides, soil nutrient (N, P, K) contents and the level of soil acidity.

Estimation of the dispersal distances of an aphid-borne virus in a patchy landscape

Characterising the spatio-temporal dynamics of pathogensin naturais key to ensuring their efficient prevention and control. However, it is notoriously difficult to estimate dispersal parameters at scales that are relevant to real epidemics. Epidemiological surveys can provide informative data, but parameter estimation can be hampered when the timing of the epidemiological events is uncertain, and in the presence of interactions between disease spread, surveillance, and control. Further complications arise from imperfect detection of disease, and from the computationally intractable number of data on individual hosts arising from landscape-level surveys. Here, we present a Bayesian framework that overcomes these barriers by integrating over associated uncertainties in a model explicitly combining the processes of disease dispersal, surveillance and control. Using a novel computationally efficient approach to account for patch geometry, we demonstrate that disease dispersal distances can be estimated accurately in a fragmented landscape when disease control is ongoing. Applying this model to data for an aphid-borne virus (Plum pox virus) surveyed for 15 years over 600 orchards, we obtain the first estimate of the distribution of the flight distances of infectious aphids at the landscape scale. Most infectious aphids leaving a tree land beyond the bounds of a 1-ha orchard (50% of flights terminate within about 90 m). Moreover, long-distance flights are not rare (10% of flights exceed 1 km). By their impact on our quantitative understanding of winged aphids dispersal, these results can inform the design of management strategies for plant viruses, which are mainly aphid-borne.Author SummaryIn spatial epidemiology, dispersal kernels quantify how the probability of pathogen dissemination varies with distance. Spatial models of pathogen spread are sensitive to kernel parameters; yet these parameters have rarely been estimated using field data gathered at relevant scales. Robust estimation is rendered difficult by practical constraints limiting the number of surveyed individuals, and uncertainties concerning their disease status. Here, we present a framework that overcomes these barriers and permits inference for a between-patch transmission model. Extensive simulations show that dispersal kernels can be estimated from epidemiological surveillance data. When applied to such data collected from more than 600 orchards during 15 years of a plant virus epidemic our approach enables the estimation of the dispersal kernel of infectious winged aphids. This kernel is long-tailed, as 50% of the infectious aphids leaving a tree terminate their infectious flight within 90 m and 10% beyond 1 km. This first estimate of flight distances at the landscape scale for aphids–a group of vectors transmitting numerous viruses–is crucial for the science-based design of control strategies targeting plant virus epidemics.

Spatio-temporal patterns and movement analysis of pigs from smallholder farms and implications for African swine fever spread, Limpopo province, South Africa

Infectious and zoonotic disease outbreaks have been linked to increasing volumes of legal and illegal trade. Spatio-temporal and trade network analyses have been used to evaluate the risks associated with these challenges elsewhere, but few details are available for the pig sector in South Africa. Regarding pig diseases, Limpopo province is important as the greater part of the province falls within the African swine fever control area. Emerging small-scale pig farmers in Limpopo perceived pig production as an important means of improving their livelihood and an alternative investment. They engage in trading and marketing their products with a potential risk to animal health, because the preferred markets often facilitate potential longdistance spread and disease dispersal over broad geographic areas. In this study, we explored the interconnectedness of smallholder pig farmers in Limpopo, determined the weaknesses and critical control points, and projected interventions that policy makers can implement to reduce the risks to pig health. The geo-coordinates of surveyed farms were used to draw maps, links and networks. Predictive risks to pigs were determined through the analyses of trade networks, and the relationship to previous outbreaks of African swine fever was postulated. Auction points were identified as high-risk areas for the spread of animal diseases. Veterinary authorities should prioritise focused surveillance and diagnostic efforts in Limpopo. Early disease detection and prompt eradication should be targeted and messages promoting enhanced biosecurity to smallholder farmers are advocated. The system may also benefit from the restructuring of marketing and auction networks. Since geographic factors and networks can rapidly facilitate pig disease dispersal over large areas, a multi-disciplinary approach to understanding the complexities that exist around the animal disease epidemiology becomes mandatory.

Spatio-Temporal Dynamics of Arabica Coffee Berry Disease Caused by Colletotrichum kahawae on a Plot Scale

Coffee berry disease (CBD) is caused by Colletotrichum kahawae. This pathogen only attacks green berries; it causes cherry rot and premature fruit fall. The disease leads to major harvest losses in the western highland region of Cameroon. The origin of the primary inoculum and the beginning of epidemics are unknown. The interactions between the pathogen and its host were studied at locations where CBD was known to cause severe disease. The disease was monitored weekly in uniform plots of adjacent coffee trees at Santa (1,750 m) in 2003 and 2004 and Bafou (1,820 m) in 2004 and 2005. The logistic model provided good fit of the epidemic's temporal dynamics. The spatial distribution of CBD over time indicated that plants in a plot were contaminated stepwise from the first infected coffee tree. An analysis of semi-variograms and the disease dispersal maps obtained by kriging revealed primary infection foci at both sites. They were observed from the 8th to the 10th week after flowering at Bafou and from the 11th to the 13th week at Santa. CBD affected the entire plots 3 weeks after the foci first appeared. These results suggest that inoculum from previous epidemics survives at points in the initial foci in a coffee plantation.