How can we forecast outbreaks of transboundary migratory pests?

Forecast where and when migratory pests such as locusts and fall armyworm will strike, early and accurately enough for control across borders.

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1	# How can we forecast outbreaks of transboundary migratory pests?
2
3	Desert locusts and fall armyworm can cross borders within a season, riding the winds and devastating crops where they land. Warning farmers before they arrive means predicting where pests will breed and where the winds will carry them.
4
5	## Question
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7	What strategies can establish disaster-response mechanisms and precise early warning for major transboundary migratory pests, such as the desert locust, the fall armyworm and migratory moths? This covers monitoring (field surveys, radar, remote sensing), models of breeding habitat and migration, the skill of forecasts, and the institutions that turn warnings into timely control across borders.
8
9	## Background
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11	The question is in the theme "enabling smart and controlled-environment agriculture for food security" of the 100 essential questions for the future of agriculture, compiled from 419 questions submitted worldwide in 2022. Elsewhere the authors point to the growing frequency of extreme weather and of pest and disease outbreaks as threats to agricultural productivity [1].
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13	## What is known
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15	- Radar over southern England showed that about 3.5 trillion high-flying insects, 3,200 tonnes of biomass, migrate over the region each year, and that insects heavier than 10 mg exploit seasonally favourable tailwinds [2].
16	- After fall armyworm spread through sub-Saharan Africa, where maize yield losses of 20–50% were estimated, species distribution models identified South and Southeast Asia and Australia as climatically suitable and at high risk of invasion along trade routes [3].
17	- Trajectory simulations predicted that fall armyworm would migrate from Indochina and southern China into China's main maize regions by two pathways, reaching the Huang-Huai-Hai region in three to four generations, by June or July [4].
18	- During the 2020 desert locust upsurge in East Africa, the largest there in more than 70 years, a machine-learning model trained on 9,134 locust records predicted high-risk breeding areas in Kenya, Sudan, Uganda and South Sudan [5].
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20	## What would count as progress
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22	No single result settles a question like this; contributions like these would move it forward:
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24	- Forecasts issued in advance and verified against later field observations, reporting hit rates, false alarms and lead times for at least one pest across several seasons.
25	- Open, cross-border datasets that combine survey, radar and trap records with weather data, to train and test migration models.
26	- Evaluations of whether early warning reduced crop losses or pesticide use compared with reactive control.
27
28	## References
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30	1. Hu, Y. et al. (2023). [100 essential questions for the future of agriculture](https://doi.org/10.1002/moda.5). Modern Agriculture 1(1), 4–12.
31	2. Hu, G. et al. (2016). [Mass seasonal bioflows of high-flying insect migrants](https://doi.org/10.1126/science.aah4379). Science 354(6319), 1584–1587.
32	3. Early, R. et al. (2018). [Forecasting the global extent of invasion of the cereal pest Spodoptera frugiperda, the fall armyworm](https://doi.org/10.3897/neobiota.40.28165). NeoBiota 40, 25–50.
33	4. Li, X.-J. et al. (2020). [Prediction of migratory routes of the invasive fall armyworm in eastern China using a trajectory analytical approach](https://doi.org/10.1002/ps.5530). Pest Management Science 76(2), 454–463.
34	5. Kimathi, E. et al. (2020). [Prediction of breeding regions for the desert locust Schistocerca gregaria in East Africa](https://doi.org/10.1038/s41598-020-68895-2). Scientific Reports 10, 11937.
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36	## Source
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38	Imported from [100 essential questions for the future of agriculture, question 74](https://doi.org/10.1002/moda.5), curated by Hu et al. Listed as open in the source (2023) and still an active research question as of 2026-10-20. The statement above is written in our own words; see the source for the original wording, history and discussion.