24 citations as recorded by crossref.

1. Fertilization increased leaf water use efficiency and growth of Pinus taeda subjected to five years of throughfall reduction L. Samuelson et al. 10.1139/cjfr-2017-0357
2. Calibration of the process-based model 3-PG for major central European tree species D. Forrester et al. 10.1007/s10342-021-01370-3
3. Effects of climate on the growth of Swiss uneven-aged forests: Combining >100 years of observations with the 3-PG model D. Forrester et al. 10.1016/j.foreco.2021.119271
4. Predicting Adaptive Genetic Variation of Loblolly Pine (Pinus taeda L.) Populations Under Projected Future Climates Based on Multivariate Models M. Lu et al. 10.1093/jhered/esz065
5. Ecological forecasting of tree growth: Regional fusion of tree‐ring and forest inventory data to quantify drivers and characterize uncertainty K. Heilman et al. 10.1111/gcb.16038
6. Creating Landscape-Scale Site Index Maps for the Southeastern US Is Possible with Airborne LiDAR and Landsat Imagery R. Gopalakrishnan et al. 10.3390/f10030234
7. Linking big models to big data: efficient ecosystem model calibration through Bayesian model emulation I. Fer et al. 10.5194/bg-15-5801-2018
8. A mid‐century ecological forecast with partitioned uncertainty predicts increases in loblolly pine forest productivity R. Thomas et al. 10.1002/eap.1761
9. Macrosystems EDDIE Teaching Modules Increase Students’ Ability to Define, Interpret, and Apply Concepts in Macrosystems Ecology A. Hounshell et al. 10.3390/educsci11080382
10. Simulation of climate change and thinning effects on productivity of Larix olgensis plantations in northeast China using 3-PGmix model Y. Xie et al. 10.1016/j.jenvman.2020.110249
11. The effect of climate variability factors on potential net primary productivity uncertainty: An analysis with a stochastic spatial 3-PG model H. Restrepo et al. 10.1016/j.agrformet.2022.108812
12. r3PG – An r package for simulating forest growth using the 3‐PG process‐based model V. Trotsiuk et al. 10.1111/2041-210X.13474
13. Bayesian calibration of a carbon balance model PREBAS using data from permanent growth experiments and national forest inventory F. Minunno et al. 10.1016/j.foreco.2019.02.041
14. Realized ecological forecast through an interactive Ecological Platform for Assimilating Data (EcoPAD, v1.0) into models Y. Huang et al. 10.5194/gmd-12-1119-2019
15. Assessing the response of forest productivity to climate extremes in Switzerland using model–data fusion V. Trotsiuk et al. 10.1111/gcb.15011
16. Targeting Extreme Events: Complementing Near-Term Ecological Forecasting With Rapid Experiments and Regional Surveys M. Redmond et al. 10.3389/fenvs.2019.00183
17. Bayesian hybrid analytics for uncertainty analysis and real‐time crop management E. Meenken et al. 10.1002/agj2.20659
18. Introducing 3-PG2Py, an open-source forest growth model in Python X. Song & Y. Song 10.1016/j.envsoft.2022.105358
19. Impacts of climate change on biological rotation of Larix olgensis plantations for timber production and carbon storage in northeast China using the 3-PGmix model Y. Xie et al. 10.1016/j.ecolmodel.2020.109267
20. Biological and market responses of pine forests in the US Southeast to carbon fertilization J. Henderson et al. 10.1016/j.ecolecon.2019.106491
21. Growth and yield drivers of loblolly pine in the southeastern U.S.: A meta-analysis H. Restrepo et al. 10.1016/j.foreco.2018.12.007
22. Perspective: Advancing the research agenda for improving understanding of cyanobacteria in a future of global change M. Burford et al. 10.1016/j.hal.2019.04.004
23. Species interactions under climate change in mixed stands of Scots pine and pedunculate oak M. Bouwman et al. 10.1016/j.foreco.2020.118615
24. Evapotranspiration and water yield of a pine‐broadleaf forest are not altered by long‐term atmospheric [CO 2 ] enrichment under native or enhanced soil fertility E. Ward et al. 10.1111/gcb.14363