SummaryAn efficient random sampling method is introduced to estimate the contributions of several sources of uncertainty to prediction variance of (computer) models. Predktion uncertainty is caused by uncertainty about the initial state, parameters, unknown (e.g. future) exogenous variables, noises, etcetera. Such uncertainties are modelled here as random inputs into a detenninistic model, which translates input uncertainty into output uncertainty. The goal is ·to pinpoint the major causes of output uncertainty. The method presented is particularly suitable for cases where uncertainty is present in a large number of inputs (such as future weather conditions). The expected reduction of output variance is estimated for the case that various (groups of) inputs should become fully determined. The method can be applied if the input sources fall into stochastically independent groups. The approach is more flexible than conventional methods based on approximations of the model. An agronomic example illustrates the method. A deterministic model is used to advise farmers on control of brown rust in wheat. Empirical data were used to estimate the distributions of uncertain inputs. Analysis shows that effective improvement of the precision of the model's prediction requires alternative submodels describing pest population dynamics, rather than better determination of initial conditions and parameters.

Data from surveys of winter wheat fields in the period 1974-1986 and of seed lots in the period 1962-1986 and identifications of diseases on plant samples were compiled to describe the occurrence of snow mould (Monographella nivalis) and Fusarium spp. On average, M. nivalis dominated over Fusarium spp. The complex of Fusarium spp. constituted mainly of E culmorum, followed by E avenaceum and E graminearum. M. nivalis was dominant in May on stem-bases and in July on leaves and leaf sheaths. On seeds M. nivalis predominated only in years with low temperatures in July and August.Average brown footrot infection in the field was 4% tillers in May and 5~ culms in July. Brown footrot intensity in July was high in cropping seasons with high precipitation in October and with low temperatures in October, November and December. In July during the early eighties, an average of 8 % of leaves and 6% of flag leaf sheaths were infected by M. nivalis. Average ear blight incidence was 1.2% glumes infected. Seed contamination by these pathogens averaged 26% in the years 1962-1986. The contamination was high in years with high precipitation in June, July and August. Aspects of cv. resistance and yield loss are illustrated.

The cropping system with winter wheat has changed considerably during the past fifteen years in the Netherlands. Crop biomass has been increased by use of new cultivars and higher levels of fertilizers and pesticides. The question which arises is to what extend the level of fertilization affects epidemics of pests and diseases, compared to the well‐known effects of pesticides and cultivars. Epidemics observed in the winter wheat fields of the Development Farming System project (DFS) at Nagele, in 1984 and 1985 provided data for this analysis. Higher levels of fertilization stimulated epidemics of yellow rust, mildew, snow mould, leaf miners and cereal leaf beetles in the fields. The magnitude of the effect was comparable to those of the pesticides used and the cultivars sown. The level of fertilization showed no effect on five other species. How these interrelations between cropping practices and the associated development of pests and diseases can be used to design an integrated approach towards cereal cropping and breeding is discussed.

The components of an existing model for supervised control of aphids (especially Sitobion avenae) and brown rust (Puccinia recondita)

Results of annual surveys of winter wheat fields from 1974 to 1986 were compiled to describe epidemics of powdery mildew and rusts in relation to weather and cultivar resistance.