• We developed a Risk Index (RI) for Cassava whitefly ( Bemisia tabaci ). • The RI integrates development, reproduction and survival. • Cassava whitefly risk varies in time and space across Africa. • Peak cassava whitefly risk occurs near 27.5 °C in cassava-growing areas. • Climate change will increase cassava whitefly risk across Africa. The cassava whitefly ( Bemisia tabaci ) greatly constrains cassava production across Africa due to its role as a vector of viral diseases that cause substantial yield losses. Effective management of this insect pest requires detailed knowledge of its spatio-temporal distribution, however long-term datasets are scarce. Mechanistic models circumvent these long-term data needs by modelling temperature-dependent processes that govern population dynamics. Nevertheless, their application to B. tabaci remains poorly explored. Here, we developed a mechanistic model to derive a risk index (RI) for B. tabaci across Africa, focusing on Malawi. The model integrates the effects of temperature on the life stages of B. tabaci to predict temporal risk dynamics and assess climate change impacts. Validation against historical data demonstrated strong agreement, with high cosine similarity values (0.95 in 1988 and 0.96 in 1990) and high correlation coefficients (0.73 and 0.78 in 1988 and 1990, respectively), supporting its suitability as a proxy for whitefly population dynamics. Areas with temperatures between 20.2 °C and 32.5 °C are conducive to B. tabaci population increase, with suitability peaking near 27.5 °C. Cassava-growing regions in central and western Africa experience year-round higher RI values, whereas southeastern Africa experiences peak RI values from October to March. In Malawi, the lakeshore and southern regions were most vulnerable, with RI peaking in these areas during the rainy season. At continental and national scales, climate change is projected to increase RI values. These findings underscore the importance of timing pest control interventions to align with peak risk periods and highlight the utility of mechanistic models for informing region-specific whitefly management strategies.
Banda et al. (Thu,) studied this question.