Sensitivity analysis of C and N modules in biogeochemical crop and grassland models following manure addition to soil

Abstract

Process‐based crop and grassland models estimating carbon (C) and nitrogen (N) dynamics are widely used to investigate best management practices in agriculture. They integrate several processes in a complex structure, but studies where modules corresponding to specific processes extracted from the whole model structure are assessed independently are uncommon. With the support of documented aerobic incubation trials in manure‐amended soils, a sensitivity analysis was performed on the C–N cycling processes of four modules (MOD1–4), corresponding to the models APSIM, EPIC, FASSET and STICS. The results showed that the parameter ‘substrate use efficiency’ had the most effect on the predicted values of net CO2 emissions and net N mineralization, together with the C/N ratio of the soil microbial biomass. They explained 74–75% on average of both output variances, whereas parameters determining manure C and N partitioning and first‐order decomposition constants of manure pools explained, on average, an additional 17–19%. Efforts should be focused on calibrating these parameters for more accurate simulations. The greater sensitivity of both outputs to parameters related to manure pools in more complex modules (MOD2–4) facilitates their adaptation to specific contexts, whereas MOD1 probably requires that parameters related to soil pools are also adapted to specific applications. Parameter interactions were limited, becoming noticeable only in situations of N‐limited soil organic matter decomposition. Models MOD1 and MOD3 allowed the C/N ratio of the soil microbial biomass to vary temporarily; therefore, they were less sensitive to mineral N availability and more easily adapted to a wide range of situations. This study provides essential information to support the development of state‐of‐the‐art biogeochemical models. Highlights We compared four C–N modules embedded in process‐based biogeochemical models. We used sensitivity analysis to assess the simulation of manure decomposition in soil. We identified a few parameters that influenced CO2 emissions and N mineralization. We found that substrate use efficiency explained most of the output variance for all models.

Ensemble modelling, uncertainty and robust predictions of organic carbon in long‐term bare‐fallow soils

“…A varying number of pools (often including inert and slow‐decomposing organic matter and microbial biomass) linked by first‐order equations is usually simulating both C and N fluxes within and between each pool (Falloon & Smith, 2010). However, different models vary considerably in the underlying assumptions and C processes in current models, for example, regarding number of pools, type of decomposition kinetics used and processes regulating SOC retention (Cavalli et al., 2019; Manzoni & Porporato, 2009).…”

“…A varying number of pools (often including inert and slow-decomposing organic matter and microbial biomass) linked by first-order equations is usually simulating both C and N fluxes within and between each pool (Falloon & Smith, 2010). However, different models vary considerably in the underlying assumptions and C processes in current models, for example, regarding number of pools, type of decomposition kinetics used and processes regulating SOC retention (Cavalli et al, 2019;Manzoni & Porporato, 2009). Each model offers a distinctive synthesis of scientific knowledge (Brilli et al, 2017) and multi-model ensembles developed from several models may reduce uncertainties in biological and physical outputs that occur over large scales, such as regions and continents (e.g.…”

Uncertainties in the adaptation of alpine pastures to climate change based on remote sensing products and modelling

“…Regarding C-N fluxes, the two grassland models agreed to some extent across study areas and pasture macro-types, with differences in magnitude and patterns likely associated with the inherent structure of each specific C and N sub-model (e.g. Cavalli et al, 2019). The higher C uptake Ceschia et al, 2010;Zeeman et al, 2010Zeeman et al, , 2019.…”

A review on the interplay between bioeconomy and soil organic carbon stocks maintenance.

“…Large variability in the predictions has been shown across models when using the same conditions [140], thus it complicates the selection of a model. Various authors have reviewed the generalities, limitations, accuracy, and sensitivity of different models without a clear answer on how to define that a model would perform better than other [136], [138], [140]- [143]. Multi-model comparison studies have revealed that using an ensemble of models (i.e., 13 [144]; 2-4 with site-specific calibration [139]) may provide more reliable results than single models.…”