An Integrated Modeling Approach for Predicting Potential Epidemics of Bacterial Blossom Blight in Kiwifruit under Climate Change

Abstract

The increasing variation in climatic conditions under climate change directly influences plant-microbe interactions. To account for as many variables as possible that may play critical roles in such interactions, the use of an integrated modeling approach is necessary. Here, we report for the first time a local impact assessment and adaptation study of future epidemics of kiwifruit bacterial blossom blight (KBB) in Jeonnam province, Korea, using an integrated modeling approach. This study included a series of models that integrated both the phenological responses of kiwifruit and the epidemiological responses of KBB to climatic factors with a 1 km resolution, under the RCP8.5 climate change scenario. Our results indicate that the area suitable for kiwifruit cultivation in Jeonnam province will increase and that the flowering date of kiwifruit will occur increasingly earlier, mainly due to the warming climate. Future epidemics of KBB during the predicted flowering periods were estimated using the Pss-KBB Risk Model over the predicted suitable cultivation regions, and we found location-specific, periodic outbreaks of KBB in the province through 2100. Here, we further suggest a potential, scientifically-informed, long-term adaptation strategy using a cultivar of kiwifruit with a different maturity period to relieve the pressures of future KBB risk. Our results clearly show one of the possible options for a local impact assessment and adaptation study using multiple models in an integrated way.

Keywords: Korea; adaptation; integrated modeling; kiwifruit; local impact assessment.

Fig. 1

Elevation map of the study area, Jeonnam province in Korea. Major counties growing kiwifruits in the province are labelled on the map: JD, Jindo; HN, Haenam; WD, Wando; GJ, Gangjin; JH, Jangheung; BS, Boseong; GH, Goheung; SC, Suncheon; YS, Yeosu; and GY, Gwangyang. Note that the 7 Automated Synoptic Observing System (ASOS) stations used for the downscaling of 11 GCM scenarios are starred on the map.

Fig. 2

Integrated modeling workflow using three models in the study. First, the Climate Suitability Model generates suitable areas for kiwifruit cultivation. Then, the Chill-day Model simulates flowering period of kiwifruit vines only on the suitable areas in Jeonnam province identified by the Climate Suitability Model. Lastly, the Pss-KBB Risk Model simulates potential epidemic risks of KBB using the flowering dates generated by the Chill-day Model as an input. Pss, Pseudomonas syringae pv. syringae; KBB, kiwifruit bacterial blossom blight.

Fig. 3

Climatic suitability maps for kiwifruit cultivation under the RCP 8.5 climate change scenario in Jeonnam province, Korea. GMC, Gwangju Metropolitan City.

Fig. 4

Simulated severity of KBB compared to observed severity in 2015. (A) Simulated R scores (Sim. R scores) by the Pss-KBB Risk Model were converted into disease severity (Sim. severity), and then compared with the observed disease severities (Obs. severity). Weather information was collected from the weather stations near each investigated orchard. (B) Mean absolute error (MAE) and root-mean-square error (RMSE) were calculated for both simulated disease severity and observed disease severity. Pss, Pseudomonas syringae pv. syringae; KBB, kiwifruit bacterial blossom blight.

Fig. 5

Potential epidemic risks of KBB in areas of Jeonnam province suitable for kiwifruit cultivation under the RCP 8.5 climate change scenario for each 10-y period from the historical (2000–2009) to the future (2020–2029–2040–2049, 2060–2069, and 2080–2089) periods. The frequencies of severe epidemics for each 10-year period were calculated based on the simulation results of the Pss-KBB Risk Model, with disease severity greater than 20% per year considered to be a severe epidemic. (A) Spatio-temporal trend of the KBB epidemics shown on the map of Jeonnam province. Note that the light gray areas on the map indicate the “Not suitable” areas for kiwifruit cultivation in each decadal period, which were not subject to the simulation of KBB epidemics. (B) Comparison of the mean KBB severe epidemics on 7 Automated Synoptic Observing System (ASOS) station locations in Jeonnam province (stars in Fig. 1), simulated with the Korea Meteorological Administration 1 km scenario (solid line), the observed weather data of the 7 ASOS stations only for 2000–2009 (round dot), or the downscaled scenarios of 11 GCMs (dotted line with gray shared area). Note that the dotted line indicates the mean of the simulated KBB severe epidemics from 11 GCMs, and the gray shared area indicates the range of ± 1 SD from the mean. Pss, Pseudomonas syringae pv. syringae; KBB, kiwifruit bacterial blossom blight.

Fig. 6

Predicted changes in severe epidemics of kiwifruit blossom blight (KBB risk) and flowering dates in Jeonnam province from the 2000s to the 2090s under the RCP8.5 climate change scenario. The decadal changes of the KBB risk (black line) and the flowering date (black dotted line) for kiwifruit cultivar Hayward were compared with those of the KBB risk (gray line) and the flowering date (gray dotted line) of the kiwifruit cultivar Haegeum for the same periods (2000s–2090s). The bottom graph indicates the decadal changes of average values of temperature (°C) and rainfall frequency (days) during the 10-day periods before full bloom of Hayward (black graphs) and Haegeum (gray graphs) cultivars.