New agricultural soil model approaches based on the
microbiome dynamics must be considered as they can contribute to understand
microbiological soil processes directly linked to substrate metabolism and the
influence of these processes on plant growth. The present work presents an
approach to the modelling of the interactions of the soil microbial functional
diversity with the plant in terms of
functions associated to specific processes of organic Carbon and
Nitrogen metabolism. The substrates transformations arising in the organic
matter that enters as a part of an agricultural scheme are the base for define
this metabolism. As result, it has been possible to simulate a rhizospheric
soil based on the concept of complex system dynamics and Individual Based Modeling
known too as Agent-Based Modeling in an agricultural management context. The
explicit definition of the microbiome functional diversity and the processing
of the structural elements Carbon and Nitrogen, allowed representing the
functional dynamics of this complex system composed by microorganisms, Carbon,
Nitrogen and the plant. The variables that reflect the biology and the
adaptation to the rhizospheric environment characterized the microorganisms and the assemblage community patterns in
time. The main simulations output are system glucose and nitrate levels and an
approach to plant growth, all resultant from the metabolic process of the
considered Carbon and Nitrogen consortia. The results indicate that the microorganism’s diversity assemblages and its
functional expression have a fundamental role in terms of plant growth.
References
[1]
Baveye, P.C., Berthelin, J. and Munch, J.C. (2016) Too Much or Not Enough: Reflection on Two Contrasting Perspectives on Soil Biodiversity. Soil Biology and Biochemistry, 103, 320-326. https://doi.org/10.1016/j.soilbio.2016.09.008
[2]
Louis, B.P., et al. (2016) Microbial Diversity Indexes Can Explain Soil Carbon Dynamics as a Function of Carbon Source. PLoS ONE, 11, e0161251.
https://doi.org/10.1371/journal.pone.0161251
[3]
Maron, P.A., et al. (2018) High Microbial Diversity Promotes Soil Ecosystem Functioning. Applied and Environmental Microbiology, 84, e02738-17.
[4]
Berlemont, R. (2017) Distribution and Diversity of Enzymes for Polysaccharide Degradation in Fungi. Scientific Reports, 7, Article No. 222.
https://doi.org/10.1038/s41598-017-00258-w
[5]
Berlemont, R. and Martiny, A.C. (2015) Genomic Potential for Polysaccharide Deconstruction in Bacteria. Applied and Environmental Microbiology, 81, 1513-1519.
[6]
Berlemont, R. and Martiny, A.C. (2016) Glycoside Hydrolases across Environmental Microbial Communities. PLoS Computational Biology, 12, e1005300.
https://doi.org/10.1371/journal.pcbi.1005300
Karimian, E. and Motamedian, E. (2020) ACBM: An Integrated Agent and Constraint Based Modeling Framework for Simulation of Microbial Communities. Scientific Reports, 10, Article No. 8695. https://doi.org/10.1038/s41598-020-65659-w
[9]
Quaghebeur, W. (2017) Individual-Based Modelling of Biodiversity in Microbial Communities. Master’s Thesis, Universiteit Gent, Ghent.
[10]
Prats, C., Ferrer, J., Gras, A. and Ginovart, M. (2010) Individual-Based Modelling and Simulation of Microbial Processes: Yeast Fermentation and Multi-Species Composting. Mathematical and Computer Modelling of Dynamical Systems, 16, 489-510.
https://doi.org/10.1080/13873954.2010.481809
[11]
Portell, X., Pot, V., Garnier, P., Otten, W. and Baveye, P.C. (2018) Microscale Heterogeneity of the Spatial Distribution of Organic Matter Can Promote Bacterial Biodiversity in Soils: Insights from Computer Simulations. Frontiers in Microbiology, 9, Article No. 1583. https://doi.org/10.3389/fmicb.2018.01583
[12]
Ginovart, M. (2014) Discovering the Power of Individual-Based Modelling in Teaching and Learning: The Study of a Predator-Prey System. Journal of Science Education and Technology, 23, 496-513. https://doi.org/10.1007/s10956-013-9480-6
[13]
Leveau, J.H.J., Hellweger, F.L., Kreft, J.U., Prats, C. and Zhang, W. (2018) Editorial: The Individual Microbe: Single-Cell Analysis and Agent-Based Modelling. Frontiers in Microbiology, 9, Article No. 2825. https://doi.org/10.3389/fmicb.2018.02825
[14]
Juyal, A., et al. (2019) Combination of Techniques to Quantify the Distribution of Bacteria in Their Soil Microhabitats at Different Spatial Scales. Geoderma, 334, 165-174. https://doi.org/10.1016/j.geoderma.2018.07.031
[15]
Jiao, S., Xu, Y., Zhang, J., Hao, X. and Lu, Y. (2019) Core Microbiota in Agricultural Soils and Their Potential Associations with Nutrient Cycling. mSystems, 4, e00313-18.
[16]
Bodine, E.N., Panoff, R.M., Voit, E.O. and Weisstein, A.E. (2020) Agent-Based Modeling and Simulation in Mathematics and Biology Education. Bulletin of Mathematical Biology, 82, Article No. 101. https://doi.org/10.1007/s11538-020-00778-z
[17]
Banitz, T., Gras, A. and Ginovart, M. (2015) Individual-Based Modeling of Soil Organic Matter in NetLogo: Transparent, User-Friendly, and Open. Environmental Modelling & Software, 71, 39-45. https://doi.org/10.1016/j.envsoft.2015.05.007
[18]
Resat, H., Bailey, V., McCue, L.A. and Konopka, A. (2012) Modeling Microbial Dynamics in Heterogeneous Environments: Growth on Soil Carbon Sources. Microbial Ecology, 63, 883-897. https://doi.org/10.1007/s00248-011-9965-x
[19]
Moore, J.C., Boone, R.B., Koyama, A. and Holfelder, K. (2014) Enzymatic and Detrital Influences on the Structure, Function, and Dynamics of Spatially-Explicit Model Ecosystems. Biogeochemistry, 117, 205-227.
https://doi.org/10.1007/s10533-013-9932-3
[20]
Yi, X., Yi, K., Fang, K., Gao, H., Dai, W. and Cao, L. (2019) Microbial Community Structures and Important Associations Between Soil Nutrients and the Responses of Specific Taxa to Rice-Frog Cultivation. Frontiers in Microbiology, 10, Article No. 1752. https://doi.org/10.3389/fmicb.2019.01752
[21]
Louis, B.P., Maron, P.A., Viaud, V., Leterme, P. and Menasseri-Aubry, S. (2016) Soil C and N Models that Integrate Microbial Diversity. Environmental Chemistry Letters, 14, 331-344. https://doi.org/10.1007/s10311-016-0571-5
[22]
Tong, L., Zhu, L., Lv, Y., Zhu, K., Liu, X. and Zhao, R. (2019) Response of Organic Carbon Fractions and Microbial Community Composition of Soil Aggregates to Long-Term Fertilizations in an Intensive Greenhouse System. Journal of Soils and Sediments, 20, 641-652. https://doi.org/10.1007/s11368-019-02436-x
[23]
Choi, J., et al. (2018) Spatial Structuring of Cellulase Gene Abundance and Activity in Soil. Frontiers in Environmental Science, 6, Article No. 107.
https://doi.org/10.3389/fenvs.2018.00107
[24]
Jeong, H.L., Goudriaan, J. and Challa, H. (2003) Using the Expolinear Growth Equation for Modelling Crop Growth in Year-Round Cut Chrysanthemum. Annals of Botany, 92, 697-708. https://doi.org/10.1093/aob/mcg195
[25]
Martin, T.N., Neto, D.D., Vieira, P.A., Pereira, A.R., Manfron, P.A. and Christoffoleti, P.J. (2012) Modelo modificado de estimacao da produtividade deplecionada e potencial da soja. Acta Scientiarum-Agronomy, 34, 369-378.