35. Modelling the Impact of Climate Change on Sustainable Management of the Codling Moth (Cydia pomonella) as Key Pest in Apple. J. Samietz1, S. Stoeckli1, ...
Modelling the Impact of Climate Change on Sustainable Management of the Codling Moth (Cydia pomonella) as Key Pest in Apple J. Samietz1, S. Stoeckli1, M. Hirschi2, C. Spirig2, H. Höhn1, P. Calanca3 and M. Rotach2 Agroscope Changins-Wädenswil, Research Station, ACW, Wädenswil, Switzerland 2 Federal Office for Meteorology and Climatology, MeteoSwiss, Zürich, Switzerland 3 Agroscope Reckenholz-Tänikon Research Station, ART, Zürich, Switzerland
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Keywords: climate change, weather generator, integrated pest management, phenology, model, codling moth Abstract Climate change in temperate regions will lead to higher and more extreme temperature distributions; however, its impact on pests and their control strategies is rarely investigated in detail. One reason is the problem of downscaling climate predictions to the temporal and spatial scale of pest life cycles. In the present study, we have closed that gap by impact modelling on hourly scale and downscaling on spatial scale, by adapting a stochastic weather generator (WG) for pest modelling. Thereby, the codling moth, Cydia pomonella, serves as a relevant model species for a key pest with multiple generations per year that, already under the present climate, requires intensive efforts to control. Stochastic weather generation, followed by a resampling approach, provided hourly synthetic weather data for 10 meteorological sites in Switzerland under future climate conditions (2045-2074). Synthetic weather was analysed by a phenology model implemented in the forecasting system SOPRA. The results showed significant shifts to earlier dates in codling moth phenological events in Swiss apple orchards, increased magnitude of the 2nd generation, less overlap between stages, and a bigger risk for an additional 3rd generation. Shifts in phenology and magnitude of later generations require adaptations of plant protection regimes to maintain their sustainability, as we illustrate in the present paper, specifically for the strategies in codling moth control. In general, however, methodologies may easily be adapted in further pest and disease combinations and cropping systems. INTRODUCTION In most parts of temperate Europe, present and projected climate change will lead to higher and more variable temperatures, and less stable precipitation regimes, charging agriculture by increased risks of drought and heat waves or flooding. Beside such direct impacts, agriculture is also largely affected by indirect effects of climate change, such as changes in the distribution and abundance of pests and diseases (e.g., Trnka et al., 2007). Although insects may regulate body temperatures in certain ranges (Samietz et al., 2005), many species – especially smaller herbivores – are nearly exclusively ectotherms (Willmer et al., 2000) and therefore, development processes, reproduction, and their distribution range are strongly determined by temperature. As a consequence, current climatic projections predict that insect species distribution will shift from lower latitudes polewards and from lower to higher altitudes. Those range shifts in insect distributions have been observed in nature as response of global warming (Walther et al., 2002; Parmesan, 2006). With the projected further temperature increase, agricultural pests and diseases are expected to occur more frequently and possibly extend to previously unaffected regions (Aluja et al., 2011). The earlier onset of the growing season and the longer season also means that already present polyvoltine species may be able to finish more generations per year and consequently, can build up higher population levels towards the end of the season (Stoeckli et al., 2012). Although increased pest outbreaks are judged “virtually certain” in the IPCC Synthesis Report (IPCC, 2007), their impact on the crops and especially on the crop protection strategies is rarely investigated in detail. One major reason is the problem of Proc. IXth IS on Modelling in Fruit Research and Orchard Management Ed.: G. Bourgeois Acta Hort. 1068, ISHS 2015
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downscaling climate predictions to the temporal and spatial scale of pest and disease life cycles. Assessing the risk of pest-related damages, as a consequence of climate change, requires information on future weather for the pest relevant habitats and on the appropriate time scale (i.e., hourly temporal resolution). Reliable information on climate change is, however, only available on coarse temporal and spatial resolution from climate models. In the present study, we have approached to close that gap by impact modelling on hourly scale and downscaling on spatial scale (Hirschi et al., 2012). The impact of climate change and variability was focused on apple, one of the most important commercial and rural crops across Europe. Thereby, codling moth, Cydia pomonella (L.), serves as a relevant model species for a pest with multiple generations per year that already, under the present climate, requires intensive efforts to control. MATERIALS AND METHODS Climate Change Scenarios The climate change information, processed in our approach, was taken from the regional climate simulations of the EU-FP6 project ENSEMBLES (Hewitt et al., 2005). This database includes 21 regional climate models (RCMs) driven by 8 global climate models (GCMs), in a transient mode covering at least the 1950-2050 time period. Here, the periods 2045-2074 vs. 1980-2009 were considered for the analysis, based on the subset of simulations (14 RCM-GCM chains with six GCMs involved), which ran beyond 2050. Using a Bayesian multi-model combination algorithm (Buser et al., 2009), the simulations were processed and aggregated to obtain seasonal probabilistic climate change signals of changes in temperature and precipitation. For details of the approach and scenario combinations, see Fischer et al. (2011) and Hirschi et al. (2012), respectively. Downscaling Procedure To downscale the seasonal climate change information to the time scales relevant for the pest (i.e., hourly weather series at 10 meteorological stations in Switzerland, see Table 1), a stochastic weather generator (WG), combined with a re-sampling approach, was applied (Dubrovský, 2011). The stations were selected based on the availability of long-term (i.e., covering the 1980-2009 control period) hourly and daily meteorological observations for the calibration of the downscaling procedure and for in situ pest observations for the validation. First, synthetic daily weather series were produced using the parametric stochastic WG M&Rfi (Dubrovský et al., 2004), a Richardson-type WG (Richardson, 1981), which is based on a Markov chain to model precipitation occurrence, Gamma distribution for the precipitation amount and an autoregressive model for non-precipitation variables (with conditioned statistics of the daily non-precipitation variables on occurrence or nonoccurrence of precipitation). In a second step, a nearest neighbour re-sampling procedure was appended to obtain hourly temporal resolution. For each day of the synthetic weather from the WG, the ten most similar days were selected from the hourly station observations within a ±10-day time window around the corresponding day of year. The similarity was quantified by the Mahalanobis distance, considering daily mean precipitation and temperature, daily temperature range, and solar radiation. From the selected ten daily cycles, one was randomly chosen. In a final step, a fitting was applied to the hourly values of precipitation, solar radiation, and temperature to match the daily values generated by the WG; finally, transitions of temperature at midnight were smoothed (Dubrovský et al., 2011). Modelling Codling Moth Phenology In order to simulate pest phenologies for application of the synthetic weather data, the codling moth model, implemented in the Swiss forecasting system for orchard pests SOPRA, was applied (Samietz et al., 2007). All models of this system are based on time36
varying distributed delay routines on hourly basis, simulating the proportion of life stages in the population with a temporal resolution of one day in the output. Due to the distributed delay approach, life stages were fully overlapping without limitations to the number of stages. All models included close approximations of the micro-climatic condition, as prerequisite for successful modelling; in case of the codling moth, the simulation of stem temperature over the season. The switch between univoltine and polyvoltine life cycles in the model was determined by a day length signal in nature and accordingly, a latitudinally adapted day of year (DOY) in the model that switches further development to diapause. The codling moth model was adapted to simulate an overwintering generation and three following generations divided into eggs, larvae, pupae and adults. The outputs of the model were used to establish indicative temperature sums for different codling moth life stages. Transferred to dates of occurrence (DOY), these indicative temperature sums permitted to compute many combinations of climate scenarios and therefore, enabled to carry out sensitivity tests exploiting the uncertainty range of the probabilistic climate change signals. For a selected range of climate change signals, the full phenology was simulated with the SOPRA model (cf., Hirschi et al., 2012). RESULTS AND DISCUSSION Validation of Downscaling In a direct validation, climate and weather statistics of the synthetic hourly weather series for present climate conditions were compared to observed statistics to ensure consistency and resulted in nearly perfect matches of observed and simulated daily weather (Hirschi et al., 2012). The downscaling approach was further validated by driving the codling moth model by the synthetic weather series of present climate and the output was then compared with observations in situ. The results show that the median occurrence of the developmental stages does not deviate between those of observed weather series and synthetic weather, based on the current climate (all weather stations: Wilcoxon-MannWhitney WMW tests for shift in location, n.s.; Kolmogorov-Smirnov KS tests for the equality probability distributions, n.s.). Results were exemplified for the locations Wädenswil and Magadino (Fig. 1, black dots vs. open black circles). Effect of Climate Change on Codling Moth Considering the median climate change signal and 100 years of synthetic data, the scenarios showed a shift of almost two weeks to an earlier flight start of codling moth, under future climate conditions, for all of the ten stations considered (WMW, KS tests, P
