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Background: The cotton jassid, Amrasca biguttula, a dangerous and polyphagous pest, has recently invaded the Middle East, Africa and South America, raising concerns about the future of cotton and other food crops including okra, eggplant and potato. However, its potential distribution remains largely unknown, posing a challenge in developing effective phytosanitary strategies. We used an ensemble model of six machine-learning algorithms including random forest, maxent, support vector machines, classification and regression tree, generalized linear model and boosted regression trees to forecast the potential distribution of A.
View Article and Find Full Text PDFAdapting distribution patterns of desert locusts, in response to global climate change.
Bull Entomol Res
January 2025
State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing 100193, P.R. China.
The desert locust () is a destructive migratory pest, posing great threat to over 60 countries globally. In the backdrop of climate change, the habitat suitability of desert locusts is poised to undergo alterations. Hence, investigating the shifting dynamics of desert locust habitats holds profound significance in ensuring global agricultural resilience and food security.
View Article and Find Full Text PDFSeasonal forecasting of East African short rains.
Sci Rep
January 2025
Department of Biological, Geological, and Environmental Sciences, University of Bologna, Bologna, Italy.
The variability of East African short rains (October-December) has profound socioeconomic and environmental impacts on the region, making accurate seasonal rainfall predictions essential. We evaluated the predictability of East African short rains using model ensembles from the multi-system seasonal retrospective forecasts from the Copernicus Climate Change Service (C3S). We assess the prediction skill for 1- to 5-month lead times using forecasts initialized in September for each year from 1993 to 2016.
View Article and Find Full Text PDFIncreasing certainty in projected local extreme precipitation change.
Nat Commun
January 2025
School of Physical Science and Technology, Yangzhou University, Yangzhou, China.
The latest climate models project widely varying magnitudes of future extreme precipitation changes, thus impeding effective adaptation planning. Many observational constraints have been proposed to reduce the uncertainty of these projections at global to sub-continental scales, but adaptation generally requires detailed, local scale information. Here, we present a temperature-based adaptative emergent constraint strategy combined with data aggregation that reduces the error variance of projected end-of-century changes in annual extremes of daily precipitation under a high emissions scenario by >20% across most areas of the world.
View Article and Find Full Text PDFSpatiotemporal analysis of agricultural drought variability in the uMkhanyakude District Municipality, KwaZulu-Natal.
Environ Monit Assess
January 2025
Department of Life and Consumer Sciences, University of South Africa, Johannesburg, South Africa.
Exploring drought dynamics has become urgent due to unprecedented climate change. Projections indicate that drought events will become increasingly widespread globally, posing a significant threat to the sustainability of the agricultural sector. This growing challenge has resulted in heightened interest in understanding drought dynamics and their impacts on agriculture.
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