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@Article{Taghizadeh-Toosi2014,
  author   = {Arezoo Taghizadeh-Toosi and Bent T. Christensen and Nicholas J. Hutchings and Jonas Vejlin and Thomas Kaetterer and Margaret Glendining and Jorgen E. Olesen},
  title    = {{C-TOOL: A simple model for simulating whole-profile carbon storage in temperate agricultural soils}},
  journal  = {Ecological Modelling},
  year     = {2014},
  volume   = {292},
  pages    = {11 - 25},
  issn     = {0304-3800},
  abstract = {Soil organic carbon (SOC) is a significant component of the global carbon (C) cycle. Changes in SOC storage affect atmospheric CO2 concentrations on decadal to centennial timescales. The C-TOOL model was developed to simulate farm- and regional-scale effects of management on medium- to long-term SOC storage in the profile of well-drained agricultural mineral soils. C-TOOL uses three SOC pools for both the topsoil (0–25cm) and the subsoil (25–100cm), and applies temperature-dependent first order kinetics to regulate C turnover. C-TOOL also enables the simulation of 14C turnover. The simple model structure facilitates calibration and requires few inputs (mean monthly air temperature, soil clay content, soil C/N ratio and C in organic inputs). The model was parameterised using data from 19 treatments drawn from seven long-term field experiments in the United Kingdom, Sweden and Denmark. It was found that the initial SOC content had to be optimised for each experiment, but also that one set of values for other model parameters could be applied at all sites. With this set of parameters, C-TOOL can be applied more widely to evaluate effects of management options on SOC storage in temperate agricultural soils. C-TOOL simulates observed losses of SOC in soils under intensive agricultural use and the gain in SOC derived from large inputs of animal manure and inclusion of perennial grassland. The model simulates changes in SOC for the entire profile, but lack of data on subsoil SOC storage hampers a proper model evaluation. Experimental verification of management effects on subsoil C storage, subsoil C inputs from roots, and vertical transport of C in the soil profile remains prioritised research areas.},
  doi      = {10.1016/j.ecolmodel.2014.08.016},
  keywords = {Soil carbon storage, Modelling, C-TOOL, Agriculture, Management, Mineral soils},
}

@Article{Tuomi2009,
  author   = {M. Tuomi and T. Thum and H. Jaervinen and S. Fronzek and B. Berg and M. Harmon and J.A. Trofymow and S. Sevanto and J. Liski},
  title    = {{Leaf litter decomposition-Estimates of global variability based on Yasso07 model}},
  journal  = {Ecological Modelling},
  year     = {2009},
  volume   = {220},
  number   = {23},
  pages    = {3362 - 3371},
  issn     = {0304-3800},
  abstract = {Litter decomposition is an important process in the global carbon cycle. It accounts for most of the heterotrophic soil respiration and results in formation of more stable soil organic carbon (SOC) which is the largest terrestrial carbon stock. Litter decomposition may induce remarkable feedbacks to climate change because it is a climate-dependent process. To investigate the global patterns of litter decomposition, we developed a description of this process and tested the validity of this description using a large set of foliar litter mass loss measurements (nearly 10,000 data points derived from approximately 70,000 litter bags). We applied the Markov chain Monte Carlo method to estimate uncertainty in the parameter values and results of our model called Yasso07. The model appeared globally applicable. It estimated the effects of litter type (plant species) and climate on mass loss with little systematic error over the first 10 decomposition years, using only initial litter chemistry, air temperature and precipitation as input variables. Illustrative of the global variability in litter mass loss rates, our example calculations showed that a typical conifer litter had 68% of its initial mass still remaining after 2 decomposition years in tundra while a deciduous litter had only 15% remaining in the tropics. Uncertainty in these estimates, a direct result of the uncertainty of the parameter values of the model, varied according to the distribution of the litter bag data among climate conditions and ranged from 2% in tundra to 4% in the tropics. This reliability was adequate to use the model and distinguish the effects of even small differences in litter quality or climate conditions on litter decomposition as statistically significant.},
  doi      = {10.1016/j.ecolmodel.2009.05.016},
  keywords = {Bayesian inference, Climate change, Decomposition, Leaf litter, Markov chain Monte Carlo, Soil carbon},
}

@Article{Sierra2012,
  author  = {Sierra, C. A. and Mueller, M. and Trumbore, S. E.},
  title   = {{Models of soil organic matter decomposition: the SoilR package, version 1.0}},
  journal = {Geoscientific Model Development},
  year    = {2012},
  volume  = {5},
  number  = {4},
  pages   = {1045--1060},
  doi     = {10.5194/gmd-5-1045-2012},
}

@Manual{Mersmann2015,
  title  = {{microbenchmark: Accurate Timing Functions}},
  author = {Mersmann, O.},
  year   = {2015},
  note   = {R package version 1.4-7},
  url    = {https://CRAN.R-project.org/package=microbenchmark},
}

@Manual{Eddelbuettel2021,
  title  = {{Rcpp: Seamless R and C++ Integration}},
  author = {Dirk Eddelbuettel and Romain Francois and JJ Allaire and Kevin Ushey and Qiang Kou and Nathan Russell and Douglas Bates and John Chambers},
  year   = {2021},
  note   = {R package version 1.0.6},
  url    = {https://CRAN.R-project.org/package=Rcpp},
}

@Manual{Eddelbuettel2021a,
  title  = {{RcppArmadillo: 'Rcpp' Integration for the 'Armadillo' Templated Linear Algebra Library}},
  author = {Dirk Eddelbuettel and Romain Francois and Doug Bates and Binxiang Ni},
  year   = {2021},
  note   = {R package version 0.10.4.0.0},
  url    = {https://CRAN.R-project.org/package=RcppArmadillo},
}

@Manual{Viechtbauer2021,
  title  = {{mathjaxr: Using 'Mathjax' in Rd Files}},
  author = {Wolfgang Viechtbauer},
  year   = {2021},
  note   = {R package version 1.4-0},
  url    = {https://CRAN.R-project.org/package=mathjaxr},
}

@Manual{Sierra2014,
  title  = {{SoilR: Models of Soil Organic Matter Decomposition}},
  author = {Carlos A. Sierra and Markus Mueller},
  year   = {2014},
  note   = {R package version 1.1-23},
  url    = {https://CRAN.R-project.org/package=SoilR},
}

@Manual{Soetaert2020,
  title  = {{deSolve: Solvers for Initial Value Problems of Differential Equations ('ODE', 'DAE', 'DDE')}},
  author = {Karline Soetaert and Thomas Petzoldt and R. Woodrow Setzer},
  year   = {2020},
  note   = {R package version 1.28},
  url    = {https://CRAN.R-project.org/package=deSolve},
}

@Manual{Boshnakov2021,
  title  = {{Rdpack: Update and Manipulate Rd Documentation Objects}},
  author = {Georgi N. Boshnakov},
  year   = {2021},
  note   = {R package version 2.1.1},
  url    = {https://CRAN.R-project.org/package=Rdpack},
}

@InProceedings{Coleman1996,
  author    = {Coleman, K. and Jenkinson, D. S.},
  title     = {{RothC-26.3 - A Model for the turnover of carbon in soil}},
  booktitle = {Evaluation of Soil Organic Matter Models},
  year      = {1996},
  editor    = {Powlson, David S. and Smith, Pete and Smith, Jo U.},
  pages     = {237--246},
  address   = {Berlin, Heidelberg},
  publisher = {Springer Berlin Heidelberg},
  abstract  = {Rothc-26.3 is a model of the turnover of organic carbon in non-waterlogged soils that allows for the effects of soil type, temperature, moisture content and plant cover on the turnover process. It uses a monthly time step to calculate total organic carbon (t C ha-1), microbial biomass carbon (t C ha-1) and $\Delta$14C (from which the radiocarbon age of the soil can be calculated) on a years-to-centuries timescale (Jenkinson, 1990; Jenkinson and Coleman, 1994; Jenkinson et al., 1987; Jenkinson et al., 1991; Jenkinson et al., 1992). It needs few inputs and those it needs are easily obtainable. It is an extension of the earlier model described by Jenkinson and Rayner (1977), and by Hart (1984). Needless to say, it has many ideas in common with other contemporary turnover models, notably CENTURY (Parton et al., 1988) and Van Veen and Paul's model (Van Veen and Paul, 1981).},
  isbn      = {978-3-642-61094-3},
}

@Article{Viskari2020,
  author  = {Viskari, T. and Laine, M. and Kulmala, L. and Maekelae, J. and Fer, I. and Liski, J.},
  title   = {{Improving Yasso15 soil carbon model estimates with ensemble adjustment Kalman filter state data assimilation}},
  journal = {Geoscientific Model Development},
  year    = {2020},
  volume  = {13},
  number  = {12},
  pages   = {5959--5971},
  doi     = {10.5194/gmd-13-5959-2020},
}

@Article{Viskari2022,
  author  = {Viskari, T. and Pusa, J. and Fer, I. and Repo, A. and Vira, J. and Liski, J.},
  title   = {{Calibrating the soil organic carbon model Yasso20 with multiple datasets}},
  journal = {Geoscientific Model Development},
  year    = {2022},
  volume  = {15},
  number  = {4},
  pages   = {1735--1752},
  doi     = {10.5194/gmd-15-1735-2022},
}

@Comment{jabref-meta: databaseType:bibtex;}
