59 citations as recorded by crossref.

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2. Representing anthropogenic gross land use change, wood harvest, and forest age dynamics in a global vegetation model ORCHIDEE-MICT v8.4.2 C. Yue et al. https://doi.org/10.5194/gmd-11-409-2018
3. Modelling vegetation dynamics in managed grasslands: Responses to drivers depend on species richness T. Moulin et al. https://doi.org/10.1016/j.ecolmodel.2018.02.013
4. Integrating large mammalian herbivores into Dynamic Global Vegetation Models: Status and next steps Y. Cheng et al. https://doi.org/10.1111/1365-2435.70409
5. Modeling European ruminant production systems: Facing the challenges of climate change R. Kipling et al. https://doi.org/10.1016/j.agsy.2016.05.007
6. Modeling grazing effects on carbon dynamics in alpine grasslands of the Qinghai-Tibetan Plateau using the Biome-BGCMuSo model L. Zheng et al. https://doi.org/10.1016/j.agee.2025.110056
7. Effect of climate change, CO2 trends, nitrogen addition, and land‐cover and management intensity changes on the carbon balance of European grasslands J. Chang et al. https://doi.org/10.1111/gcb.13050
8. The greenhouse gas balance of European grasslands J. Chang et al. https://doi.org/10.1111/gcb.12998
9. Simulating tropical forage growth and biomass accumulation: an overview of model development and application A. Andrade et al. https://doi.org/10.1111/gfs.12177
10. Modelling the role of livestock grazing in C and N cycling in grasslands with LPJmL5.0-grazing J. Heinke et al. https://doi.org/10.5194/gmd-16-2455-2023
11. A model-data fusion approach to analyse carbon dynamics in managed grasslands V. Myrgiotis et al. https://doi.org/10.1016/j.agsy.2020.102907
12. Inferring management and predicting sub-field scale C dynamics in UK grasslands using biogeochemical modelling and satellite-derived leaf area data V. Myrgiotis et al. https://doi.org/10.1016/j.agrformet.2021.108466
13. Influence of high-latitude warming and land-use changes in the early 20th century northern Eurasian CO2sink A. Bastos et al. https://doi.org/10.1088/1748-9326/aac4d3
14. Movement drives population dynamics of one of the most mobile ungulates on Earth: Insights from a mechanistic model T. Stratmann et al. https://doi.org/10.1002/ecy.4071
15. Estimating the GPP of olive trees with variable canopy cover by the use of Sentinel-2 MSI images M. Chiesi et al. https://doi.org/10.1016/j.eja.2022.126618
16. Benchmarking the seasonal cycle of CO2 fluxes simulated by terrestrial ecosystem models S. Peng et al. https://doi.org/10.1002/2014GB004931
17. Implications of accounting for management intensity on carbon and nitrogen balances of European grasslands J. Blanke et al. https://doi.org/10.1371/journal.pone.0201058
18. Grazing and aridity reduce perennial grass abundance in semi-arid rangelands – Insights from a trait-based dynamic vegetation model M. Pfeiffer et al. https://doi.org/10.1016/j.ecolmodel.2018.12.013
19. Climate warming from managed grasslands cancels the cooling effect of carbon sinks in sparsely grazed and natural grasslands J. Chang et al. https://doi.org/10.1038/s41467-020-20406-7
20. Wetter environment and increased grazing reduced the area burned in northern Eurasia from 2002 to 2016 W. Hao et al. https://doi.org/10.5194/bg-18-2559-2021
21. Risk and vulnerability of Mongolian grasslands under climate change B. Nandintsetseg et al. https://doi.org/10.1088/1748-9326/abdb5b
22. Causal relationships versus emergent patterns in the global controls of fire frequency I. Bistinas et al. https://doi.org/10.5194/bg-11-5087-2014
23. Parameterizations of human activities in paleoclimate modeling: Current status and future perspectives X. Zhang et al. https://doi.org/10.1007/s11430-025-1877-y
24. Simulating measurable ecosystem carbon and nitrogen dynamics with the mechanistically defined MEMS 2.0 model Y. Zhang et al. https://doi.org/10.5194/bg-18-3147-2021
25. Grassland yield estimations – potentials and limitations of remote sensing in comparison to process-based modeling and field measurements S. Reinermann et al. https://doi.org/10.5194/bg-22-4969-2025
26. Multi-model simulation of soil temperature, soil water content and biomass in Euro-Mediterranean grasslands: Uncertainties and ensemble performance R. Sándor et al. https://doi.org/10.1016/j.eja.2016.06.006
27. Comparing process-based models with the inventory approach to predict CH4 emission of livestock enteric fermentation J. Zhang et al. https://doi.org/10.1088/1748-9326/acb6a8
28. Climate warming and forest expansion significantly enhance China’s forest methane sink Y. Deng et al. https://doi.org/10.1016/j.agrformet.2025.110935
29. How does management affect soil C sequestration and greenhouse gas fluxes in boreal and temperate forests? – A review R. Mäkipää et al. https://doi.org/10.1016/j.foreco.2022.120637
30. Modeled Changes in Potential Grassland Productivity and in Grass-Fed Ruminant Livestock Density in Europe over 1961–2010 J. Chang et al. https://doi.org/10.1371/journal.pone.0127554
31. The carbon budget of the managed grasslands of Great Britain – informed by earth observations V. Myrgiotis et al. https://doi.org/10.5194/bg-19-4147-2022
32. The large mean body size of mammalian herbivores explains the productivity paradox during the Last Glacial Maximum D. Zhu et al. https://doi.org/10.1038/s41559-018-0481-y
33. Global and regional phosphorus budgets in agricultural systems and their implications for phosphorus-use efficiency F. Lun et al. https://doi.org/10.5194/essd-10-1-2018
34. Modelling spatio-temporal patterns of soil carbon and greenhouse gas emissions in grazing lands: Current status and prospects J. Wang et al. https://doi.org/10.1016/j.scitotenv.2020.139092
35. APSIM-Tropical Pasture: A model for simulating perennial tropical grass growth and its parameterisation for palisade grass (Brachiaria brizantha) C. Bosi et al. https://doi.org/10.1016/j.agsy.2020.102917
36. Modelling of grassland fluxes in Europe: Evaluation of two biogeochemical models R. Sándor et al. https://doi.org/10.1016/j.agee.2015.09.001
37. Productivity and CO 2 Exchange of Great Plains Ecoregions. I. Shortgrass Steppe: Flux Tower Estimates T. Gilmanov et al. https://doi.org/10.1016/j.rama.2017.06.007
38. The use of biogeochemical models to evaluate mitigation of greenhouse gas emissions from managed grasslands R. Sándor et al. https://doi.org/10.1016/j.scitotenv.2018.06.020
39. Representing Grasslands Using Dynamic Prognostic Phenology Based on Biological Growth Stages: Part 2. Carbon Cycling K. Haynes et al. https://doi.org/10.1029/2018MS001541
40. Validation of 3D-CMCC Forest Ecosystem Model (v.5.1) against eddy covariance data for 10 European forest sites A. Collalti et al. https://doi.org/10.5194/gmd-9-479-2016
41. Disentangling competitive vs. climatic drivers of tropical forest mortality M. Pillet et al. https://doi.org/10.1111/1365-2745.12876
42. Combining European Earth Observation products with Dynamic Global Vegetation Models for estimating Essential Biodiversity Variables M. Dantas de Paula et al. https://doi.org/10.1080/17538947.2019.1597187
43. Use, calibration and verification of agroecological models for boreal environments: A review D. Forster et al. https://doi.org/10.1002/glr2.12010
44. Models meet data: Challenges and opportunities in implementing land management in Earth system models J. Pongratz et al. https://doi.org/10.1111/gcb.13988
45. Combining livestock production information in a process-based vegetation model to reconstruct the history of grassland management J. Chang et al. https://doi.org/10.5194/bg-13-3757-2016
46. Sensitivity of the annual net ecosystem exchange to the cospectral model used for high frequency loss corrections at a grazed grassland site O. Mamadou et al. https://doi.org/10.1016/j.agrformet.2016.06.008
47. Grassland gross carbon dioxide uptake based on an improved model tree ensemble approach considering human interventions: global estimation and covariation with climate W. Liang et al. https://doi.org/10.1111/gcb.13592
48. Impact of mowing frequency and temperature on the production of temperate grasslands: explanations received by an individual‐based model J. Schmid et al. https://doi.org/10.1111/oik.09108
49. Impacts of land-use change on biospheric carbon: an oriented benchmark using the ORCHIDEE land surface model T. Dinh et al. https://doi.org/10.5194/gmd-17-6725-2024
50. Vegetation Growth Models Improve Surface Layer Flux Simulations of a Temperate Grassland C. Klein et al. https://doi.org/10.2136/vzj2017.03.0052
51. An improved estimate of soil carbon pool and carbon fluxes in the Qinghai-Tibetan grasslands using data assimilation with an ecosystem biogeochemical model R. Zhao et al. https://doi.org/10.1016/j.geoderma.2022.116283
52. Impacts of fertilization on grassland productivity and water quality across the European Alps under current and warming climate: insights from a mechanistic model M. Botter et al. https://doi.org/10.5194/bg-18-1917-2021
53. Explicit Representation of Grazing Activity in a Diagnostic Terrestrial Model: A Data‐Process Combined Scheme Y. Chen et al. https://doi.org/10.1029/2018MS001352
54. Future drought risk and adaptation of pastoralism in Eurasian rangelands B. Nandintsetseg et al. https://doi.org/10.1038/s41612-024-00624-2
55. Modeling vegetation and carbon dynamics of managed grasslands at the global scale with LPJmL 3.6 S. Rolinski et al. https://doi.org/10.5194/gmd-11-429-2018
56. Future productivity and phenology changes in European grasslands for different warming levels: implications for grassland management and carbon balance J. Chang et al. https://doi.org/10.1186/s13021-017-0079-8
57. Species‐genotypic parameters of the CROPGRO Perennial Forage Model: Implications for comparison of three tropical pasture grasses D. Pequeno et al. https://doi.org/10.1111/gfs.12329
58. Simulation of timothy nutritive value: A comparison of three process-based models T. Persson et al. https://doi.org/10.1016/j.fcr.2018.11.008
59. Empirical and dynamic approaches for modelling the yield and N content of European grasslands M. Dellar et al. https://doi.org/10.1016/j.envsoft.2019.104562