45 citations as recorded by crossref.

1. A Study on Meteorological Recognition With a Fusion Enhanced Model Based on EVA02 and LinearSVC C. Cheng et al.
2. Past and future wheat yield losses in France’s breadbasket R. Nóia-Júnior et al.
3. Climate normals shape regional disparities of cotton yield failures compared to dominant impacts from climate extremes S. Liu et al.
4. Projecting impacts of extreme weather events on crop yields using LASSO regression J. Heilemann et al.
5. Mitigating crop modeling uncertainties through machine learning in drylands M. Nouri & S. Veysi
6. Extreme lows of wheat production in Brazil R. Nóia Júnior et al.
7. Machine-learning methods to assess the effects of a non-linear damage spectrum taking into account soil moisture on winter wheat yields in Germany M. Peichl et al.
8. Coupling Process-Based Crop Model and Extreme Climate Indicators with Machine Learning Can Improve the Predictions and Reduce Uncertainties of Global Soybean Yields Q. Sun et al.
9. Predicting years with extremely low gross primary production from daily weather data using Convolutional Neural Networks A. Marcolongo et al.
10. Increase of Simultaneous Soybean Failures Due To Climate Change H. Goulart et al.
11. The effects of varying drought-heat signatures on terrestrial carbon dynamics and vegetation composition E. Tschumi et al.
12. Risk of rice production failure in India under climate change C. Bowden et al.
13. A critical synthesis of remote sensing and machine learning approaches for climate hazard impact on crop yield S. Obahoundje et al.
14. Accounting for Weather Variability in Farm Management Resource Allocation in Northern Ghana: An Integrated Modeling Approach O. Adelesi et al.
15. Analysis of the spatiotemporal evolution pattern of the synergistic relationship among high temperature, drought, and CHTDs in climate transition zones H. Su et al.
16. Linking drought indicators and crop yields through causality and information transfer: a phenology-based analysis S. Yeşilköy et al.
17. Negative impacts of climate change on crop yields are underestimated R. Nóia Júnior et al.
18. Amplified agricultural impacts from more frequent and intense sequential heat events R. Hamed et al.
19. Modeling and simulating spatial extremes by combining extreme value theory with generative adversarial networks Y. Boulaguiem et al.
20. Attribution of extreme weather event impacts on crop yields and economic damages to climate change M. Nagpal et al.
21. Synchronous climate hazards pose an increasing challenge to global coffee production D. Richardson et al.
22. Impacts of compound hot–dry extremes on US soybean yields R. Hamed et al.
23. Critical Climate Periods Explain a Large Fraction of the Observed Variability in Vegetation State A. Kern et al.
24. Two machine learning approaches for predicting cyanobacteria abundance in aquaculture ponds M. Zhang et al.
25. Greater Flash Flood Risks From Hourly Precipitation Extremes Preconditioned by Heatwaves in the Yangtze River Valley Y. Chen et al.
26. On the Systematic Occurrence of Compound Cold Spells in North America and Wet or Windy Extremes in Europe G. Messori & D. Faranda
27. Soil properties modulate actual evapotranspiration and precipitation impacts on crop yields in the USA M. Suliman et al.
28. A comparison of climate drivers’ impacts on silage maize yield shock in Germany F. Stainoh et al.
29. Guidelines for Studying Diverse Types of Compound Weather and Climate Events E. Bevacqua et al.
30. Enhancing agricultural meteorology: Qualified data production through an interoperable cyber–physical system G. Langella
31. Identification of hydro-meteorological drivers for forest low greenness events in Europe P. Rivoire et al.
32. Evaluating the impact of weather variability on maize yield fluctuation for different sowing dates Y. Su et al.
33. Extreme and compound ocean events are key drivers of projected low pelagic fish biomass N. Le Grix et al.
34. Compound Hydrometeorological Extremes: Drivers, Mechanisms and Methods W. Zhang et al.
35. Machine learning in crop yield modelling: A powerful tool, but no surrogate for science G. Lischeid et al.
36. Storylines of weather-induced crop failure events under climate change H. Goulart et al.
37. The KNMI Large Ensemble Time Slice (KNMI–LENTIS) L. Muntjewerf et al.
38. Uncovering the Dynamics of Multi‐Sector Impacts of Hydrological Extremes: A Methods Overview M. de Brito et al.
39. Identifying compound weather drivers of forest biomass loss with generative deep learning M. Anand et al.
40. Compound droughts and hot extremes: Characteristics, drivers, changes, and impacts Z. Hao et al.
41. Advancing research on compound weather and climate events via large ensemble model simulations E. Bevacqua et al.
42. Current and future risk of unprecedented hydrological droughts in Great Britain W. Chan et al.
43. Compound dry and hot extremes: A review and future research pathways for India R. Guntu & A. Agarwal
44. Measuring extremes-driven direct biophysical impacts in agricultural drought damages M. Nagpal et al.
45. Winter climate preconditioning of summer vegetation extremes in the Northern Hemisphere M. Anand et al.