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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
agricultural and biological sciences
Application of adaptive cluster sampling with a data-driven stopping rule to plant disease incidence
Journal of Phytopathology, Volume 161, No. 9, Year 2013
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Description
Plant pathologists need to manage plant diseases at low incidence levels. This needs to be performed efficiently in terms of precision, cost and time because most plant infections spread rapidly to other plants. Adaptive cluster sampling with a data-driven stopping rule (ACS*) was proposed to control the final sample size and improve efficiency of the ordinary adaptive cluster sampling (ACS) when prior knowledge of population structure is not known. This study seeks to apply the ACS* design to plant diseases at various levels of clustering and incidences levels. Results from simulation study show that the ACS* is as efficient as the ordinary ACS design at low levels of disease incidence with highly clustered diseased plants and is an efficient design compared with simple random sampling (SRS) and ordinary ACS for some highly to less clustered diseased plants with moderate to higher levels of disease incidence. © 2013 Blackwell Verlag GmbH.
Authors & Co-Authors
Gattone, Stefano Antonio
Italy, Rome
Facoltà Di Economia
Esha, Mohamed
Kenya, Voi
Taita Taveta University
Mwangi, Jesse Wachira
Kenya, Njoro
Egerton University
Statistics
Citations: 6
Authors: 3
Affiliations: 3
Identifiers
Doi:
10.1111/jph.12112
ISSN:
09311785
e-ISSN:
14390434
Study Design
Cross Sectional Study
Cohort Study