A new hive model is proposed for the assessment of the distribution and fate of pesticides in the hive ecosystem. Based on the chemical used, the model draws a dynamic picture of pesticide contamination in the hive, calculating contamination trends and concentration levels in the various hive components (e.g. bees, wax and honey). The proposed model is validated using empirical data on τ-fluvalinate residues in bees, wax and honey. It predicts with good approximation both the trends over time and the contamination levels of the pesticide in the various hive components. We have developed most of the parameters and equations used in this model. Although they will require further experimental testing, they provide realistic predictions that are consistent with the experimental data. The proposed model is a useful tool for predictive purposes and improves our understanding of contamination phenomena in the hive. © INRA, DIB-AGIB and Springer Science+Business Media B.V., 2011.
Tremolada, P., Bernardinelli, I., Rossaro, B., Colombo, M., Vighi, M. (2011). Predicting pesticide fate in the hive (part 2): development of a dynamic hive model. APIDOLOGIE, 42(4), 439-456 [10.1007/s13592-011-0012-1].
Predicting pesticide fate in the hive (part 2): development of a dynamic hive model
VIGHI, MARCO
2011
Abstract
A new hive model is proposed for the assessment of the distribution and fate of pesticides in the hive ecosystem. Based on the chemical used, the model draws a dynamic picture of pesticide contamination in the hive, calculating contamination trends and concentration levels in the various hive components (e.g. bees, wax and honey). The proposed model is validated using empirical data on τ-fluvalinate residues in bees, wax and honey. It predicts with good approximation both the trends over time and the contamination levels of the pesticide in the various hive components. We have developed most of the parameters and equations used in this model. Although they will require further experimental testing, they provide realistic predictions that are consistent with the experimental data. The proposed model is a useful tool for predictive purposes and improves our understanding of contamination phenomena in the hive. © INRA, DIB-AGIB and Springer Science+Business Media B.V., 2011.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.