Regional Differences in Control Operations during the 2019–2021 Desert Locust Upsurge

Abstract

The desert locust remains a major threat to global food security. Control operations are a crucial tool to manage crisis; this research investigated the nature of control operations conducted between 2019–2021. Historical data on desert locust and control operations were obtained from the survey reports at the FAO Locust Hub and analysed with respect to survey reports, land cover types, cropland/rangeland extent and crop productivity data. We found that 16.1% of the grid cells with locust presence and 14.9% of the grid cells with control operations had a proportion of rangeland higher than 0.75; while 13.3% of the grid cells with locust presence and 13.2% of the grid cells with control operations had a proportion of croplands higher than 0.75, highlighting that locust presence and control operations were reported in both rangeland and cropland. Control operations continue to be used both to reduce overall locust numbers and to protect crops. Furthermore, through identifying which crops were most at risk, our analyses indicate that wheat production was under the highest strain during periods of increased locust infestations.

What Have We Learned after Millennia of Locust Invasions?

“…Control operations are naturally a crucial tool for managing locust crises. Retkute et al [78] use the example of the recent Desert Locust upsurge in 2019-2021 to analyze the nature of control operations conducted to contain the crisis. These treatments were used both for the immediate protection of crops and to reduce overall locust numbers.…”

Bayesian Inference for Multiple Datasets

“…For our analysis, we considered a domain extending across five sub-Saharan countries (Kenya, Ethiopia, Somalia, Eritrea and Djibouti). This area was selected because it was affected by the 2019-2021 desert locust upsurge [36]. First, to gauge the computational effort required, we used IS to recover parameter values from synthetic data for a single dataset.…”

Bayesian Inference for Multiple Datasets

“…For our analysis we considered a domain extending across five sub-Saharan countries (Kenya, Ethiopia, Somalia, Eritrea and Djibouti). This area was selected because it was affected by the 2019-2021 desert locust upsurge [22]. First, to gauge the computational effort required, we use importance sampling to recover parameter values from synthetic data for a single dataset.…”