title: Improving detection probabilities for pests in stored grain.
abstract: BACKGROUND
  The presence of insects in stored grain is a significant problem for grain farmers, bulk grain handlers and distributors worldwide. Inspection of bulk grain commodities is essential to detect pests and thereby to reduce the risk of their presence in exported goods. It has been well documented that insect pests cluster in response to factors such as microclimatic conditions within bulk grain. Statistical sampling methodologies for grain, however, have typically considered pests and pathogens to be homogeneously distributed throughout grain commodities. In this paper, a sampling methodology is demonstrated that accounts for the heterogeneous distribution of insects in bulk grain.
  
  
  RESULTS
  It is shown that failure to account for the heterogeneous distribution of pests may lead to overestimates of the capacity for a sampling programme to detect insects in bulk grain. The results indicate the importance of the proportion of grain that is infested in addition to the density of pests within the infested grain. It is also demonstrated that the probability of detecting pests in bulk grain increases as the number of subsamples increases, even when the total volume or mass of grain sampled remains constant.
  
  
  CONCLUSIONS
  This study underlines the importance of considering an appropriate biological model when developing sampling methodologies for insect pests. Accounting for a heterogeneous distribution of pests leads to a considerable improvement in the detection of pests over traditional sampling models.
authors: Discipline of Biogeosciences, Queensland University of Technology, Brisbane, Queensland, Australia.
authors: David
authors: D
authors: Elmouttie
authors: Andreas
authors: A
authors: Kiermeier
authors: Grant
authors: G
authors: Hamilton
mesh_terms: D000818:Animals; D002523:Edible Grain / Q000469:parasitology*; D005506:Food Contamination / Q000032:analysis / Q000706:statistics & numerical data*; D007313:Insecta / Q000502:physiology; D008954:Models, Biological; D010571:Pest Control / Q000706:statistics & numerical data*; D011336:Probability; D018401:Sample Size; D012494:Sampling Studies