10 citations as recorded by crossref.

1. How does inclusion of weather forecasting impact in-season crop model predictions? K. Togliatti et al. https://doi.org/10.1016/j.fcr.2017.09.008
2. Field scale spatial wheat yield forecasting system under limited field data availability by integrating crop simulation model with weather forecast and satellite remote sensing R. Dhakar et al. https://doi.org/10.1016/j.agsy.2021.103299
3. The GGCMI Phase 2 emulators: global gridded crop model responses to changes in CO2, temperature, water, and nitrogen (version 1.0) J. Franke et al. https://doi.org/10.5194/gmd-13-3995-2020
4. Skillful seasonal prediction of key carbon cycle components: NPP and fire risk P. Bett et al. https://doi.org/10.1088/2515-7620/ab8b29
5. Disentangling the separate and confounding effects of temperature and precipitation on global maize yield using machine learning, statistical and process crop models X. Yin et al. https://doi.org/10.1088/1748-9326/ac5716
6. Evaluation of JULES-crop performance against site observations of irrigated maize from Mead, Nebraska K. Williams et al. https://doi.org/10.5194/gmd-10-1291-2017
7. Implementation of sequential cropping into JULESvn5.2 land-surface model C. Mathison et al. https://doi.org/10.5194/gmd-14-437-2021
8. Optimizing machine learning-based site-specific nitrogen application recommendations for canola production G. Wen et al. https://doi.org/10.1016/j.fcr.2022.108707
9. Vegetation Growth Models Improve Surface Layer Flux Simulations of a Temperate Grassland C. Klein et al. https://doi.org/10.2136/vzj2017.03.0052
10. The GGCMI Phase 2 experiment: global gridded crop model simulations under uniform changes in CO2, temperature, water, and nitrogen levels (protocol version 1.0) J. Franke et al. https://doi.org/10.5194/gmd-13-2315-2020