Estimating the Risk of a Crop Epidemic From Coincident Spatio-temporal Processes

Abstract

Fusarium Head Blight (FHB) or 'scab' is a very destructive disease that affects wheat crops. Recent research has resulted in accurate weather-driven models that estimate the probability of an FHB epidemic based on experiments. However, these predictions ignore two crucial aspects of FHB epidemics: (1) An epidemic is very unlikely to occur unless the plants are flowering, and (2) FHB spreads by its spores, resulting in spatial and temporal dependence in risk. We develop a new approach that combines existing weather-based probabilities with information on flowering dates from survey data, while simultaneously accounting for spatial and temporal dependence. Our model combines two space-time processes, one associated with pure weather-based FHB risks and the other associated with flowering date probabilities. To allow for scalability, we model spatiotemporal dependence via a process convolutions approach. Our sample based approach produces a realistic assessment of areas that are persistently at high risk (the probability of an epidemic is elevated for extended time periods), along with associated estimates of uncertainty. We conclude with the application of our approach to a case study from North Dakota.

Model-Based Forecasting of Agricultural Crop Disease Risk at the Regional Scale, Integrating Airborne Inoculum, Environmental, and Satellite-Based Monitoring Data

“…Diseases like stripe rust (Puccinia striiformis f. sp. tritici) (Pst) and fusarium head blight (Fusarium graminearum) (FHB) on wheat, and powdery mildew (Erysiphe necator) on grapes, to highlight just a few, cause major crop losses globally (Hovmøller, 2001;Carisse et al, 2009;Haran et al, 2010;Newberry et al, 2016). Plant breeding to increase host resistance remains the primary approach for managing diseases and to help sustainable agricultural yields, as crop breeding networks that deploy resistance genes decrease the likelihood that pathogens will overcome resistance (Ojiambo et al, 2017).…”

“…Model uncertainty and reliability remain two major issues challenging the development of more robust and effective quantitative approaches for disease management. To address these aspects requires an integrated model-based framework and statistical approach that can integrate new types of data, model spatial and temporal dependence and interaction, quantify uncertainties, evaluate multiple scenarios, and bridge empirical-theory knowledge gaps (Held et al, 2004;Contreras-Medina et al, 2009;Haran et al, 2010;Savage and Renton, 2014;Kouadio and Newlands, 2015;Riley et al, 2015;Newlands, 2016;Höhle et al,, 2017;Ojiambo et al, 2017). Dennis (1987) derived a simple, multivariate regression-based model of Pst disease infection based on air temperature and surface wetness period.…”

Constructing valid spatial processes on the sphere using kernel convolutions

“…Under the DPC construction, w.s/ is a correlated Gaussian spatial process on the sphere with E.w.s// D 0 for all s 2 S 2 and C .w.s 1 /; w.s 2 // D 2 w k 0 .s 1 /k.s 2 / > 0 for any s 1 ; s 2 2 S 2 . The DPC models have seen wide spread success and applicability for spatial processes on the plane (Higdon et al, 1999;Calder, 2007;Lemos and Sanso, 2009;Haran et al, 2010;Calder et al, 2011). One key reason for this success is that the discrete formulation offers dimension reduction for large spatial datasets.…”

“…The DPC models have seen wide spread success and applicability for spatial processes on the plane (Higdon et al , ; Calder, ; Lemos and Sanso, ; Haran et al , ; Calder et al , ). One key reason for this success is that the discrete formulation offers dimension reduction for large spatial datasets.…”

Markov Chain Monte Carlo: Can We Trust the Third Significant Figure?

“…We consider a data set on wheat crop flowering dates in the state of North Dakota (Haran et al, 2007). These data consist of experts' model-based estimates for the dates when wheat crops flower at 365 different locations across the state.…”