Narrow Results

The sterile insect technique (SIT) is a biological control technique that can be used either to eliminate or decay a wild mosquito population under a given threshold to reduce the nuisance or the epidemiological risk. In this work, we propose a model using a differential system that takes into account the variations of rainfall and temperature over time and study their impacts on sterile males’ releases strategies. Our model is as simple as possible to avoid complexity while being able to capture the temporal variations of an Aedes albopictus mosquito population in a domain treated by SIT, located in Réunion island. The main objective is to determine what period of the year is the most suitable to start a SIT control to minimize the duration of massive releases and the number of sterile males to release, either to reduce the mosquito nuisance, or to reduce the epidemiological risk. Since sterilization is not $100\%$ efficient, we also study the impact of different levels of residual fertility within the released sterile males population. Our study shows that rainfall plays a major role in the dynamics of the mosquito and the SIT control, that the best period to start a massive SIT treatment lasts from July to December, that residual fertility has to be as small as possible, at least for nuisance reduction. Indeed, when the main objective is to reduce the epidemiological risk, we show that residual fertility is not necessarily an issue. Increasing the size of the releases is not always interesting. We also highlight the importance of combining SIT with mechanical control, i.e., the removal of breeding sites, in particular when the initial mosquito population is large. Last but not least our study shows the usefulness of the modeling approach to derive various simulations to anticipate issues and demand in terms of sterile insects’ production.

The North American Multi-Model Ensemble (NMME) has grown into a fully developed scientific database for seasonal and sub-seasonal climate forecasts, progressing prediction from global to regional scales. The NMME has continuously developed, with new models replacing old ones; it is hypothesized that this development will generate more accurate forecasts over time. However, to date, this hypothesis has not been verified in Central Africa (CA). This study investigates the hypothesis that the skill of NMME models will increase as the forecasting system advances, focusing on rainfall in CA. The study is conducted for the four configuration (phases) of NMME models, from the oldest to the most recent. The analyses are performed with Short Lead (SL) time and Long Lead (LL) time hindcasts very coherent with the perspectives of the CA. The results show from configuration 1 (phase 1) to configurations 4 (phase 4), the NMME models reasonably replicate the spatial structures in the seasonal rainfall climatology of the observations with a remarkable bias at LL. The mean absolute error and root mean square difference reveal small but incremental improvements in the prediction skills of NMME models from phase 1 to phase 4. The Pearson coefficient (r) increased in SL by about 1%, i.e., from 0.94 to 0.95 during June–August (JJA) season and about 4% during the September–November (SON), i.e., from $r=0.90$ in phase 1 to $r=0.94$ in phase 4, about 3% from phase 1 to phase 4 during the March–May (MAM). The categorical scores show that the Probability of Detection (POD) and False Alarm (FAR) increased very slightly from phase 1 to phase 4, but is it noted that the different combinations of the NMME forecasting system present difficulties in predicting rainy and dry events. It should be added that by introducing newer models into a multi-model ensemble as they are developed, and by eliminating older models, small skill gains are observed in the NMME forecasting system in CA.

Climate change poses mounting risks to agricultural development and rural livelihoods in Nigeria. This study investigates the impacts of climate change on agricultural sector employment in Nigeria. Agriculture provides income and sustenance for much of Nigeria’s rural population. However, smallholder rain-fed farming predominates, with minimal resilience to climate shifts. Historical data reveal rising temperatures and declining, erratic rainfall across Nigeria’s agro-ecological zones since the 1970s. Crop modeling predicts further climate changes will reduce yields of key staple crops. This threatens the viability of smallholder agriculture and risks widespread job losses. The study adopts a nonlinear autoregressive distributed lag (NARDL) modeling approach to evaluate climate change effects on agricultural sector employment in Nigeria from 1990 to 2020. Findings reveal reduced rainfall initially raises employment, as farming requires more labor in dry conditions. However, protracted droughts significantly reduce agricultural jobs. Increased temperatures consistently lower farm employment through reduced yields and incomes. Based on these findings, the study recommends that adaptive strategies are urgently needed to build resilience, promote climate-smart agriculture, and safeguard rural livelihoods.