Posts tagged photosynthesis
LPJmL - Lund-Potsdam-Jena managed Land
- 12 February 2026
LPJmL is a global dynamic vegetation, hydrology, and carbon–nitrogen biogeochemistry model for gridded land-surface simulations (run offline or coupled to climate models). It simulates photosynthesis, stomatal conductance, surface energy balance, vegetation dynamics and competition, phenology, carbon allocation, soil organic matter turnover, and river discharge. Land use and management processes—cropland, pasture, irrigation, and harvest—are explicitly represented. Nutrient limitations and nitrogen cycling influence productivity, carbon storage, and greenhouse gas fluxes. Recent extensions include process-based methane dynamics, linking wetland and rice hydrology to vertically resolved CH₄ production, oxidation, and transport (diffusion, ebullition, and plant-mediated), yielding consistent CH₄, CO₂, N, and water budgets.
AgroC
- 21 May 2024
AgroC is a 1-d model of water, energy and matter fluxes in agricultural systems operating at hourly or daily time steps, accounting for organic carbon, nitrogen and phosphorus turnover and soil mineral nitrogen and phosphorus pools. The SoilCO2/RothC model (Herbst et al., 2008; Simunek et al., 1993) was extended with the dynamic plant growth module SUCROS (Spitters et al., 1988). Combining those subroutines allows for a closed carbon balance of cropped ecosystems at an hourly or daily time step. The model explicitly accounts for soil carbon turnover, soil CO2 flux, plant water stress, nutrient stress and organ-specific carbon allocation. Standard crop input parameters exist for cereals, sugar beet, maize, potato and grassland. It was successfully validated for various sites and crops (Klosterhalfen et al., 2017) and the latest implementations comprise soil nitrous gas emissions. AgroC has been applied to simulate the water stress dependent within-field variability of carbon fluxes (Herbst et al., 2021), to model variability of leaf are index and yield at the 1km2 scale (Brogi et al., 2020; Brogi et al., 2021) and it was part of a large crop model intercomparison study (Groh et al., 2020; Groh et al., 2022). Classical agronomical applications are documented for maize (Zydelis et al., 2018) and hemp (Zydelis et al., 2022). Future scenarios of maize cropping under climate change were investigated with AgroC by Zydelis et al., (2021). A quite unique feature of this model is the estimation of leaf-level solar induced fluorescence in dependence of water stress (DeCanniere et al., 2021).