Downscaling climate change scenarios for apple pest and disease modeling in Switzerland

Abstract

As a consequence of current and projected climate change in temperate regions of Europe, agricultural pests and diseases are expected to occur more frequently and possibly to extend to previously non-affected regions. Given their economic and ecological relevance, detailed forecasting tools for various pests and diseases have been developed, which model their phenology, depending on actual weather conditions, and suggest management decisions on that basis. Assessing the future risk of pest-related damages requires future weather data at high temporal and spatial resolution. Here, we use a combined stochastic weather generator and re-sampling procedure for producing site-specific hourly weather series representing present and future (1980–2009 and 2045–2074 time periods) climate conditions in Switzerland. The climate change scenarios originate from the ENSEMBLES multi-model projections and provide probabilistic information on future regional changes in temperature and precipitation. Hourly weather series are produced by first generating daily weather data for these climate scenarios and then using a nearest neighbor re-sampling approach for creating realistic diurnal cycles. These hourly weather series are then used for modeling the impact of climate change on important life phases of the codling moth and on the number of predicted infection days of fire blight. Codling moth (<i>Cydia pomonella</i>) and fire blight (<i>Erwinia amylovora</i>) are two major pest and disease threats to apple, one of the most important commercial and rural crops across Europe. Results for the codling moth indicate a shift in the occurrence and duration of life phases relevant for pest control. In southern Switzerland, a 3rd generation per season occurs only very rarely under today's climate conditions but is projected to become normal in the 2045–2074 time period. While the potential risk for a 3rd generation is also significantly increasing in northern Switzerland (for most stations from roughly 1% on average today to over 60% in the future for the median climate change signal of the multi-model projections), the actual risk will critically depend on the pace of the adaptation of the codling moth with respect to the critical photoperiod. To control this additional generation, an intensification and prolongation of control measures (e.g. insecticides) will be required, implying an increasing risk of pesticide resistances. For fire blight, the projected changes in infection days are less certain due to uncertainties in the leaf wetness approximation and the simulation of the blooming period. Two compensating effects are projected, warmer temperatures favoring infections are balanced by a temperature-induced advancement of the blooming period, leading to no significant change in the number of infection days under future climate conditions for most stations

Integrated assessment of climate change impact on surface runoff contamination by pesticides

“…The increase in temperature does not significantly affect the number of applications against both diseases considered (Figure 4). These findings corroborate those reported by Hirschi et al (2012) for fire blight.…”

“…Some studies have suggested that the number of generations of codling moth per year could further Acc e p ted P r e p r i nt increase in the future (e.g., Luedeling et al 2011;Hirschi et al 2012;Juszczak et al 2013). Plum curculio is one of the most important pests for pome and stone fruits in eastern North America (Chouinard 2014b;Hock et al 2014).…”

“…Giayetto and Rossini (2011) observed an increase in the risk of fire blight infection on pear in Argentina during the 1970-2009 period. Hirschi et al (2012) used climate model data to evaluate the evolution of the risk of infection in Switzerland. They concluded that the infection period would start earlier in the future , but the risk of infection would remain stable.…”

The Assessment of Sustainability Indexes and Climate Change Impacts on Integrated Water Resource Management

“…Dynamical models for downscaling are as follows: HadCM3, RegCM, CRCM, RSM, MM5, and WRF [28]. Statistical downscaling could be done with a weather generator (WG), and many WG have been proposed based on the Markov-chain model [27].…”

“…The objective of downscaling is to adapt the results obtained from large-scale models into smaller scales in order to apply these data to impact studies. Statistical models and dynamical models represent the most common approaches for downscaling [27]. Dynamical models for downscaling are as follows: HadCM3, RegCM, CRCM, RSM, MM5, and WRF [28].…”