Management of plant health risks associated with processing of plant-based wastes: A review

The rise in international trade of plants and plant products has increased the risk of introduction and spread of plant pathogens and pests. In addition, new risks are arising from the implementation of more environmentally friendly methods of biodegradable waste disposal, such as composting and anaerobic digestion. As these disposal methods do not involve sterilisation, there is good evidence that certain plant pathogens and pests can survive these processes. The temperature/time profile of the disposal process is the most significant and easily defined factor in controlling plant pathogens and pests. In this review, the current evidence for temperature/time effects on plant pathogens and pests is summarised. The advantages and disadvantages of direct and indirect process validation for the verification of composting processes, to determine their efficacy in destroying plant pathogens and pests in biowaste, are discussed. The availability of detection technology and its appropriateness for assessing the survival of quarantine organisms is also reviewed

Screening potential pests of Nordic coniferous forests associated with trade in ornamental plants

Plant pests moved along with the trade in ornamental plants could pose a threat to forests. In this study plant pests potentially associated with this pathway were screened to identify pests that could pose a high risk to the coniferous forests of Finland, Sweden and Norway. Specifically, the aim was to find pests that potentially could fulfil the criteria to become regulated as quarantine pests. EPPO’s commodity study approach, which includes several screening steps, was used to identify the pests that are most likely to become significant pests of Picea abies or Pinus sylvestris. From an initial list of 1062 pests, 65 pests were identified and ranked using the FinnPRIO model, resulting in a top list of 14 pests, namely Chionaspis pinifoliae, Coleosporium asterum s.l., Cytospora kunzei, Dactylonectria macrodidyma, Gnathotrichus retusus, Heterobasidion irregulare, Lambdina fiscellaria, Orgyia leucostigma, Orthotomicus erosus, Pseudocoremia suavis, Tetropium gracilicorne, Toumeyella parvicornis, Truncatella hartigii and Xylosandrus germanus. The rankings of the pests, together with the collected information, can be used to prioritize pests and pathways for further assessment

Winter oilseed rape Evaluation of fungicide spray programmes

SIGLEGBUnited Kingdo

Oilseed rape Sclerotinia risk forecasting

SIGLEGBUnited Kingdo

Effects of light leaf spot (Pyrenopeziza brassicae) on yield of winter oilseed rape (Brassica napus)

The relationship between development of light leaf spot and yield loss in winter oilseed rape was analysed, initially using data from three experiments at sites near Aberdeen in Scotland in the seasons 1991/92, 1992/93 and 1993/94, respectively. Over the three seasons, single-point models relating yield to Light leaf spot incidence (% plants with leaves with light leaf spot) at GS 3.3 (flower buds visible) generally accounted for more of the variance than single-point models at earlier or later growth stages. Only in 1992/93, when a severe light leaf spot epidemic developed on leaves early in the season, did the single-point model for disease severity on leaves at GS 3.5/4.0 account for more of the variance than that for disease incidence at GS 3.3. In 1991/92 and 1992/3, when reasonably severe epidemics developed on stems, the single-point model for light leaf spot incidence (stems) at GS 6.3 accounted for as much of the variance. Two-point (disease severity at GS 3.3 and GS 4.0) and AUDPC models (disease incidence/severity) accounted for more of the variance than the single-point model based on disease incidence at GS 3.3 in 1992/93 but not in the other two seasons. Therefore, a simple model using the light leaf spot incidence at GS 3.3 (x) as the explanatory variable was selected as a predictive model to estimate % yield loss (y(r)): y(r) = 0.32x - 0.57. This model fitted all three data sets from Scotland. When data sets from Rothamsted, Rosemaund and Thurloxton in England were used to test it, this single-point predictive model generally fitted the data well, except when yield loss was clearly not related to occurrence of light leaf spot. However, the regression lines relating observed yield loss to light leaf spot incidence at GS 3.3 often had smaller slopes than the line produced by the model based on Scottish data.Peer reviewe