# Prediction and Surveillance Sampling Assessment in Plant Nurseries and Fields
## 4 Discussion  
First, we demonstrate that our proposed method of modeling may capture behavior patterns and draw risk maps which are similar to those obtained from the population from which the sample has been extracted. From risk maps and from estimates of the parameters, surveillance strategies and policies can be established to control the distribution of diseases in agricultural contexts. Secondly, we demonstrate that this methodology allows prediction maps of uncertainty to be built relatively simply and quickly when combining the SPDE approach with the INLA methodology. This approach facilitates the handling of large datasets with excellent computation times. The biggest advantage of our modeling compared with classical geostatistical methods is its computational benefit in performing adjustment and prediction. Another advantage of this approach is its generality, as it allows us to perform a Bayesian analysis directly and calculate criteria and various predictive measures that facilitate the comparison of complex models. Thirdly, the INLA methodology can be used in any type of spatial data, and it can even deal with continuous nonstationary and anisotropic phenomena. We also demonstrate how we obtain consistent inferences from samples in epidemiological studies that handle a significant amount of data and demonstrate the utility of the methodology in the presence of few data and in the absence of explanatory variables. It is clear that, by using Bayesian kriging, it is possible to incorporate sources of uncertainty into the model associated with the prediction parameters and thus find more realistic estimates, contrary to what happens in classical geostatistics [7]. The INLA methodology combined with the SPDE approach offers an excellent theoretical framework for phenomena that need prediction where fixed effects, structured and unstructured Gaussian random effects are combined linearly in a linear predictor, and the elements of the linear predictor are observed through one or more likelihoods [21]. The illustration of the methodology with real data allows us to recognize its usefulness in epidemiological studies, not only in the agricultural context. The Bayesian INLA-SPDE approach is a complementary method to the traditional spatial data mining approach to obtain the prediction of critical points of vulnerable species and accurately inform management decisions [23]. The proposed methodology recognizes small-scale spatial dependence, but it may be interesting to add a component or surface that can capture and improve large-scale variability and improve risk smoothing as a future line of research. Another future line of research would be tp extend a class of additive spatio-temporal SPDE models and investigate the distinction between them, (1) extending their temporal effect, allowing a random walk process in time, (2) varying the spatial correlation function, and (3) running a simulation study to evaluate the effect of erroneously specifying spatial and temporal models [24; 25]. Finally, we conclude that in situations similar to those analyzed where there are a large number of saplings to study, it is advisable to perform different sampling methods. The results we have found from the proposed modeling show it is possible to evaluate different sampling strategies, from which we can get the best results, and with which it is possible to obtain more realistic estimates and prediction maps with behaviour similar to that observed in the study population. Conceptualisation, A.L.-Q. ; Methodology, N.C.M. and A.L.-Q. ; Software, N.C.M. ; Writing--original draft preparation, N.C.M. ; Writing--review and editing, N.C.M. and A.L.-Q. ; Supervision, A.L.-Q. ; All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by grant PID2019-106341 GB-I00 from the Spanish Ministerio de Ciencia e Innovacion--Agencia Estatal de Investigacion, jointly financed by the European Regional Development Funds (ERDF). **Institutional Review Board Statement:** Not applicable.