Prediction and Surveillance Sampling Assessment in Plant Nurseries and Fields
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)....
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/18/9005 |
| _version_ | 1827663818764845056 |
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| author | Nora C. Monsalve Antonio López-Quílez |
| author_facet | Nora C. Monsalve Antonio López-Quílez |
| author_sort | Nora C. Monsalve |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T00:50:45Z |
| format | Article |
| id | doaj.art-3c0f0c1618494625904955f20d47f22a |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T00:50:45Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-3c0f0c1618494625904955f20d47f22a2023-11-23T14:51:34ZengMDPI AGApplied Sciences2076-34172022-09-011218900510.3390/app12189005Prediction and Surveillance Sampling Assessment in Plant Nurseries and FieldsNora C. Monsalve0Antonio López-Quílez1Department of Operations Research and Statistics, University Centroccidental Lisandro Alvarado, Barquisimeto 3001, VenezuelaDepartment of Statistics and Operations Research, University of Valencia, 46100 Burjassot, SpainIn 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.https://www.mdpi.com/2076-3417/12/18/9005Bayesian krigingBayesian hierarchical modelsGaussian Markov random field (GMRF)integrated nested Laplace approximation (INLA)stochastic partial differential equation (SPDE) |
| spellingShingle | Nora C. Monsalve Antonio López-Quílez Prediction and Surveillance Sampling Assessment in Plant Nurseries and Fields Applied Sciences Bayesian kriging Bayesian hierarchical models Gaussian Markov random field (GMRF) integrated nested Laplace approximation (INLA) stochastic partial differential equation (SPDE) |
| title | Prediction and Surveillance Sampling Assessment in Plant Nurseries and Fields |
| title_full | Prediction and Surveillance Sampling Assessment in Plant Nurseries and Fields |
| title_fullStr | Prediction and Surveillance Sampling Assessment in Plant Nurseries and Fields |
| title_full_unstemmed | Prediction and Surveillance Sampling Assessment in Plant Nurseries and Fields |
| title_short | Prediction and Surveillance Sampling Assessment in Plant Nurseries and Fields |
| title_sort | prediction and surveillance sampling assessment in plant nurseries and fields |
| topic | Bayesian kriging Bayesian hierarchical models Gaussian Markov random field (GMRF) integrated nested Laplace approximation (INLA) stochastic partial differential equation (SPDE) |
| url | https://www.mdpi.com/2076-3417/12/18/9005 |
| work_keys_str_mv | AT noracmonsalve predictionandsurveillancesamplingassessmentinplantnurseriesandfields AT antoniolopezquilez predictionandsurveillancesamplingassessmentinplantnurseriesandfields |