- MaCS4Plants

Here is a list of relevant publications related to Macs4Plants.

Vezy, R. (2023). PlantSimEngine: A Simulation Engine For The Soil-Plant-Atmosphere System. Journal of Open Source Software, 8(86). 5371. https://doi.org/10.21105/joss.05371

Labadie, M., Guy, K., Demené, M., Caraglio, Y., Heidsieck, G., Gaston, A., Rothan, C., Guédon, Y., Pradal, C. & Denoyes, B. (2023). Spatio-temporal analysis of strawberry architecture: insights into the control of branching and inflorescence complexity. Journal of Experimental Botany, 74(12). 3595–3612. https://doi.org/10.1093/jxb/erad097

Boursiac, Y., Pradal, C., Bauget, F., Lucas, M., Delivorias, S., Godin, C. & Maurel, C. (2022). Phenotyping and modeling of root hydraulic architecture reveal critical determinants of axial water transport. Plant Physiology, 190(2). 1289–1306. https://doi.org/10.1093/plphys/kiac281

Albasha, R., Fournier, C., Pradal, C., Chelle, M., Prieto, J., Louarn, G., Simonneau, T. & Lebon, E. (2019). HydroShoot: a functional-structural plant model for simulating hydraulic structure, gas and energy exchange dynamics of complex plant canopies under water deficit—application to grapevine (Vitis vinifera). in silico Plants, 1(1). diz007. https://doi.org/10.1093/insilicoplants/diz007

Barczi, J., Rey, H., Griffon, S. & Jourdan, C. (2018). DigR: a generic model and its open source simulation software to mimic three-dimensional root-system architecture diversity. Annals of Botany, 121(5). 1089–1104. https://doi.org/10.1093/aob/mcy018

Lobet, G., Pound, M., Diener, J., Pradal, C., Draye, X., Godin, C., Javaux, M., Leitner, D., Meunier, F., Nacry, P., Pridmore, T. & Schnepf, A. (2015). Root System Markup Language: Toward a Unified Root Architecture Description Language. Plant Physiology, 167(3). 617–627. https://doi.org/10.1104/pp.114.253625

Boudon, F., Pradal, C., Cokelaer, T., Prusinkiewicz, P. & Godin, C. (2012). L-Py: An L-System Simulation Framework for Modeling Plant Architecture Development Based on a Dynamic Language. Frontiers in Plant Science, 3. Retrieved from https://www.frontiersin.org/articles/10.3389/fpls.2012.00076

Pradal, C., Boudon, F., Nouguier, C., Chopard, J. & Godin, C. (2009). PlantGL: A Python-based geometric library for 3D plant modelling at different scales. Graphical Models, 71(1). 1–21. https://doi.org/10.1016/j.gmod.2008.10.001

Triki, H., Ribeyre, F., Pinard, F. & Jaeger, M. (2023). Coupling Plant Growth Models and Pest and Disease Models: An Interaction Structure Proposal, MIMIC. Plant Phenomics, 5. 0077. https://doi.org/10.34133/plantphenomics.0077

Fernandez, R. & Moisy, C. (2023). FijiRelax: Fast and noise-corrected estimation of MRI relaxation maps in 3D + t. Journal of Open Source Software, 8(81). 4981. https://doi.org/10.21105/joss.04981

Vaillant, J., Grechi, I., Normand, F. & Boudon, F. (2022). Towards virtual modelling environments for functional–structural plant models based on Jupyter notebooks: application to the modelling of mango tree growth and development. in silico Plants, 4(1). diab040. https://doi.org/10.1093/insilicoplants/diab040

Carrié, E., Grechi, I., Boudon, F., Frak, E., Combes, D. & Normand, F. (2023). Modeling functional relationships between morphogenetically active radiation and photosynthetic photon flux density in mango tree crown. Frontiers in Ecology and Evolution, 11. Retrieved from https://www.frontiersin.org/articles/10.3389/fevo.2023.1046332

Lemiere, L., Jaeger, M., Gosme, M. & Subsol, G. (2023). Combinatorial maps, a new framework to model agroforestry systems. Plant Phenomics, 0(ja). https://doi.org/10.34133/plantphenomics.0120

Bauget, F., Protto, V., Pradal, C., Boursiac, Y. & Maurel, C. (2023). A root functional–structural model allows assessment of the effects of water deficit on water and solute transport parameters. Journal of Experimental Botany, 74(5). 1594–1608. https://doi.org/10.1093/jxb/erac471

Midingoyi, C., Pradal, C., Enders, A., Fumagalli, D., Lecharpentier, P., Raynal, H., Donatelli, M., Fanchini, D., Athanasiadis, I., Porter, C., Hoogenboom, G., Oliveira, F., Holzworth, D. & Martre, P. (2023). Crop modeling frameworks interoperability through bidirectional source code transformation. Environmental Modelling & Software, 168. 105790. https://doi.org/10.1016/j.envsoft.2023.105790

Akbarinia, R., Botella, C., Joly, A., Masseglia, F., Mattoso, M., Ogasawara, E., Oliveira, D., Pacitti, E., Porto, F., Pradal, C., Shasha, D. & Valduriez, P. (2023). Life Science Workflow Services (LifeSWS): Motivations and Architecture. InHameurlain, A. & Tjoa, A. (Eds.), Transactions on Large-Scale Data- and Knowledge-Centered Systems LV. (pp. 1–24). Springer. https://doi.org/10.1007/978-3-662-68100-8_1

Daviet, B., Fernandez, R., Cabrera-Bosquet, L., Pradal, C. & Fournier, C. (2022). PhenoTrack3D: an automatic high-throughput phenotyping pipeline to track maize organs over time. Plant Methods, 18(1). 130. https://doi.org/10.1186/s13007-022-00961-4

Fernandez, R., Crabos, A., Maillard, M., Nacry, P. & Pradal, C. (2022). High-throughput and automatic structural and developmental root phenotyping on Arabidopsis seedlings. Plant Methods, 18(1). 127. https://doi.org/10.1186/s13007-022-00960-5

Perez, R., Vezy, R., Brancheriau, L., Boudon, F., Grand, F., Ramel, M., Artanto Raharjo, D., Caliman, J. & Dauzat, J. (2022). When architectural plasticity fails to counter the light competition imposed by planting design: an in silico approach using a functional–structural model of oil palm. in silico Plants, 4(1). diac009. https://doi.org/10.1093/insilicoplants/diac009