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Efficiency of variable‐intensity and sequential sampling for insect control decisions in cole crops in the Netherlands
Author(s) -
Shelton A. M.,
Theunissen J.,
Hoy C. W.
Publication year - 1994
Publication title -
entomologia experimentalis et applicata
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.765
H-Index - 83
eISSN - 1570-7458
pISSN - 0013-8703
DOI - 10.1111/j.1570-7458.1994.tb00749.x
Subject(s) - sampling (signal processing) , statistics , variable (mathematics) , sample (material) , intensity (physics) , sampling design , sample size determination , population , biology , sequential sampling , pest analysis , mathematics , computer science , horticulture , demography , mathematical analysis , chemistry , physics , filter (signal processing) , chromatography , quantum mechanics , sociology , computer vision , spatial distribution
A total of 24 commercial fields of cabbages and Brussels sprouts were sampled in a grid fashion with 20–25 equally spaced cells with four plants per cell. Using this data base of 80–100 plants, we conducted computer stimulations to compare the treatment decisions that would be made for the major insect pests using published sequential sampling programs and a newly developed variable‐intensity sampling program. Additionally, we compared the number of samples required to make the decision. At low thresholds (10–20%) for both Lepidoptera and cabbage aphids, variable intensity‐sampling required a smaller sample size and provided more reliable decisions, while at high thresholds (40–50%) sequential sampling provided more reliable decisions. In both procedures, the occurrence of incorrect decisions was minimal. The number of cases in which a decision would not be reached after a 40‐plant sample was lower for variable‐intensity sampling. Considering the number of samples required to make a correct decision and the greater need for reliable decisions at lower thresholds, variable‐intensity sampling was superior to sequential sampling. Additionally, variable‐intensity sampling has the advantage of requiring samples to be taken in a greater area of the field and thus increases the probability of detecting localized infestations. Although variable‐intensity sampling was not designed to classify pest populations for treatment decisions but rather to achieve sampling precision around the population mean, our present studies indicate that it can also be an effective method to aid in treatment decisions.