Abstract
BACKGROUND
The Dubas bug, Ommatissus lybicus, is a serious insect pest for the date palm Phoenix dactylifera in Oman and other date palm growing countries throughout the Middle East and North Africa. Climate is a key factor affecting the insect population dynamics, including O. lybicus. There are limited studies of O. lybicus relating to microclimate. The aim of this study was to investigate the efficiency of the various humid thermal indices (HTIs) and daily accumulation of temperature and humidity to model the relationship with O. lybicus.
RESULTS
The highly infested locations showed a strong relationship with cumulative daily mean relative humidity (RhCm), followed by daily minimum temperature (Tmin) for pooled data over two seasons. The next best regression models for the relationship between the infestation and climate was with the absolute factors, daily maximum humidity (Rhmx) and daily minimum humidity (Rhmin), compared with HTIm/m (ratio of mean humidity to mean temperature) for the autumn season. The best regression model for the spring season was daily mean humidity (Rhm) and daily maximum temperature (Tmax).
CONCLUSION
Daily cumulative temperature was the best predictive model for O. lybicus infestation on date palms, considering the microclimate data at each location separately. The predication model points to the areas that are prone to infestation and need focus for monitoring and allocation of management resources. © 2019 Society of Chemical Industry
The Dubas bug, Ommatissus lybicus, is a serious insect pest for the date palm Phoenix dactylifera in Oman and other date palm growing countries throughout the Middle East and North Africa. Climate is a key factor affecting the insect population dynamics, including O. lybicus. There are limited studies of O. lybicus relating to microclimate. The aim of this study was to investigate the efficiency of the various humid thermal indices (HTIs) and daily accumulation of temperature and humidity to model the relationship with O. lybicus.
RESULTS
The highly infested locations showed a strong relationship with cumulative daily mean relative humidity (RhCm), followed by daily minimum temperature (Tmin) for pooled data over two seasons. The next best regression models for the relationship between the infestation and climate was with the absolute factors, daily maximum humidity (Rhmx) and daily minimum humidity (Rhmin), compared with HTIm/m (ratio of mean humidity to mean temperature) for the autumn season. The best regression model for the spring season was daily mean humidity (Rhm) and daily maximum temperature (Tmax).
CONCLUSION
Daily cumulative temperature was the best predictive model for O. lybicus infestation on date palms, considering the microclimate data at each location separately. The predication model points to the areas that are prone to infestation and need focus for monitoring and allocation of management resources. © 2019 Society of Chemical Industry
| Original language | English |
|---|---|
| Pages (from-to) | 3060-3069 |
| Journal | Pest Management Science |
| Volume | 75 |
| Issue number | 11 |
| DOIs | |
| Publication status | Published - Nov 2019 |
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