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Prediction and Surveillance Sampling Assessment in Plant Nurseries and Fields

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Prediction and Surveillance Sampling Assessment in Plant Nurseries and Fields

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dc.contributor.author Monsalve, Nora C.
dc.contributor.author López Quílez, Antonio
dc.date.accessioned 2022-10-19T12:45:52Z
dc.date.available 2022-10-19T12:45:52Z
dc.date.issued 2022
dc.identifier.citation Monsalve, Nora C. López Quílez, Antonio 2022 Prediction and Surveillance Sampling Assessment in Plant Nurseries and Fields Applied Sciences-Basel 12 18 9005
dc.identifier.uri https://hdl.handle.net/10550/84211
dc.description.abstract In this paper, we propose a structured additive regression (STAR) model for modeling the occurrence of a disease in fields or nurseries. The methodological approach involves a Gaussian field (GF) affected by a spatial process represented by an approximation to a Gaussian Markov random field (GMRF). This modeling allows the building of maps with prediction probabilities regarding the presence of a disease in plants using Bayesian kriging. The advantage of this modeling is its computational benefit when compared with known spatial hierarchical models and with the Bayesian inference based on Markov chain Monte Carlo (MCMC) methods. Inference through the use of the integrated nested Laplace approximation (INLA) with the stochastic partial differential equation (SPDE) approach facilitates the handling of large datasets in excellent computation times. Our approach allows the evaluation of different sampling strategies, from which we obtain inferences and prediction maps with similar behaviour to those obtained when we consider all subjects in the study population. The analysis of the different sampling strategies allows us to recognize the relevance of spatial components in the studied phenomenon. We demonstrate how Bayesian kriging can incorporate sources of uncertainty associated with the prediction parameters, which leads to more realistic and accurate estimation of the uncertainty. We illustrate the methodology with samplings of Citrus macrophylla affected by the tristeza virus (CTV) grown in a nursery.
dc.language.iso eng
dc.relation.ispartof Applied Sciences-Basel, 2022, vol. 12, num. 18, p. 9005
dc.subject Estadística bayesiana
dc.subject Models matemàtics
dc.title Prediction and Surveillance Sampling Assessment in Plant Nurseries and Fields
dc.type journal article es_ES
dc.date.updated 2022-10-19T12:45:53Z
dc.identifier.doi 10.3390/app12189005
dc.identifier.idgrec 154984
dc.rights.accessRights open access es_ES

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