Adaptive cluster sampling with clusters selected without replacement and stopping rule

Gattone, Stefano Antonio; Mohamed, Esha; Battista, Tonio Di

doi:10.1007/s10651-016-0348-9

Cited by 6 publications

(2 citation statements)

References 18 publications

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“…The regression models developed provide predictive capacity to researchers and commercial growers about disease risk in fruiting fields, if no mitigation measures are implemented and if sampling and diagnostic protocols for QI determination can be refined. For example, this study flagged infected mother plants but, in a large nursery, the occurrence of infected mother plants is likely to be random and locating hot spots would need to be enabled by optimized sampling protocols (Gattone et al 2016).…”

Section: Discussionmentioning

confidence: 99%

Epidemiological Significance of Crown Rot in the Fruiting Field in Relation to Colletotrichum gloeosporioides Infection of Strawberry Nursery Plants

Rahman

Louws

2017

Plant Disease

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Anthracnose crown rot (ACR), caused by Colletotrichum gloeosporioides, is a serious disease of strawberry (Fragaria × ananassa) in the southeastern United States, and there is a need to determine the link between nursery and fruiting-field disease risk. A 2-year study in 2007, repeated in 2008, was conducted at the North Carolina State University Horticultural Crops Research Station, Clinton, using ‘Chandler’, the most popular cultivar in North Carolina and one that is highly susceptible to ACR. Mother plants in a summer nursery were inoculated midseason with three pathogenic strains of C. gloeosporioides at an incidence level of 0, 5, 10, or 25%. Asymptomatic runner plants were selected at maturity (85 to 88 days after inoculation) from the nursery in early to mid-October from within a 0.5-m (inner) or 0.5- to 1.0-m (outer) radius around inoculated mother plants and planted into a plasticulture fruiting field system, with fruit harvest in April to June the following spring. Plants collected from the 25%-inner treatment had the greatest area under the disease progress curve (AUDPC) values, with a terminal ACR-related plant mortality of 32 and 20% by the end of fruit harvest and marketable yield losses of 30.5 and 30.2% in 2007–08 (Yr1) and 2008–09 (Yr2) seasons, respectively. All treatments increased AUDPC values compared with noninoculated treatments except the 10%-outer (O) and 5%-O treatments in Yr1 and Yr2, respectively. Marketable yield decreased 291.6 kg/ha for every percent increase in inoculum level (i.e., 0 to 25%, R2 = 0.696, P = 0.001). Levels of quiescent infection (QI) incidence (percentage of sampled leaves) assessed 25 to 28 days before digging runner plants also directly affected yield. For example, yield decreased 131.0 kg/ha for every percent increase in QI incidence in mother plants (R2 = 0.744, P = 0.001). Immersion of plants in fungicide solutions prior to planting decreased AUDPC values and improved plant stand by 7 to 11% but did not affect marketable yield compared with controls. This study provides results that can enable nursery and fruit growers to assess risk and implement mitigation measures to limit nursery plant and fruit yield losses.

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Section: Discussionmentioning

confidence: 99%

Epidemiological Significance of Crown Rot in the Fruiting Field in Relation to Colletotrichum gloeosporioides Infection of Strawberry Nursery Plants

Rahman

Louws

2017

Plant Disease

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“…Relevant examples include expensive field studies needed to identify local maxima such as sources of pollution, areas where soil erosion is reaching critical levels, or where individuals of rare species are observed: the ability to obtain samples where the sought-after characteristics are more likely to appear, is then very important. Existing adaptive designs, such as the commonly employed Adaptive Cluster Sampling (ACS, [30]) and the more recent proposals of Adaptive Geostatistical Designs (AGD, [8], [20]), are successful in providing the desired over-representation and guarantee valid sample-based inference; however, they suffer from important drawbacks, mainly related to their producing variable sample sizes (ACS, see [1] for a discussion, and [5], [13], [14] for some recent proposals addressing the issue), to their difficulty of implementation (AGD), and to their inability to provide control on the magnitude of the over-representation (both). An extensive comparison of all of these approaches, while outside the scope of this work, could provide additional important information.…”

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confidence: 99%

A proposal for a two-step sampling design to oversample units responding to prescribed characteristics

Andreis

Bonetti

2018

Environ Ecol Stat

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We introduce a novel method to extract a sample from a finite population where units with desired characteristics are over-represented. The approach is both sequential and adaptive and allows, via suitable compositions of predictive and objective functions, to target specific subsets of the population. We consider the problem of estimation and conjecture the validity of a modified Horvitz-Thompson estimator capable to account for the imbalance induced by the targeting procedure. After discussing how to apply the method to the sampling of geographically distributed units, we investigate its potential via simulations.

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Adaptive cluster double sampling with post stratification with application to an epiphytic lichen community

Gattone

Giordani²,

Battista

et al. 2017

Environ Ecol Stat

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Adaptive cluster sampling with clusters selected without replacement and stopping rule

Cited by 6 publications

References 18 publications

Epidemiological Significance of Crown Rot in the Fruiting Field in Relation to Colletotrichum gloeosporioides Infection of Strawberry Nursery Plants

Epidemiological Significance of Crown Rot in the Fruiting Field in Relation to Colletotrichum gloeosporioides Infection of Strawberry Nursery Plants

A proposal for a two-step sampling design to oversample units responding to prescribed characteristics

Adaptive cluster double sampling with post stratification with application to an epiphytic lichen community

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