Reproducing CO2 exchange rates of a crop rotation at contrasting terrain positions using two different modelling approaches

Specka, Xenia; Nendel, Claas; Hagemann, Ulrike; Pohl, Madlen; Hoffmann, Mathias; Barkusky, Dietmar; Augustin, Jürgen; Sommer, Michael; Oost, Kristof Van

doi:10.1016/j.still.2015.05.007

Cited by 9 publications

(5 citation statements)

References 74 publications

(80 reference statements)

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“…The design choices made by the modellers during model development combined with the im perfect knowledge about biophysical processes and the short age of high-quality experimental data result in model structural and parameter uncertainties (Post, Hattermann, Krysanova, & Suckow, 2008;Tao et al, 2018). The MONICA model has been previously tested for simulating the main crops that make up the rotations of this study (see Section 2.3.1) and the effects of agricultural management on SOC (Specka et al, 2016). Nevertheless, the C input from crop residues, the relative contribution of roots and above-ground organs to soil organic matter (Kätterer, Bolinder, Andrén, Kirchmann, & Menichetti, 2011) and the simulation of SOC decomposition are important sources of uncertainty for the current study.…”

Section: Methodological Approachmentioning

confidence: 99%

“…MONICA is a process-based spatiotemporally ex plicit model which extends the crop model HERMES (Kersebaum & Richter, 1991) with the algorithms for the calculation of organic matter turnover of the DAISY model (Abrahamsen & Hansen, 2000), thus enabling the simulation of long-term effects of agricultural manage ment on SOC (e.g. Specka et al, 2016). Generic in its crop part, MONICA is designed to simulate different crops in sequence, addressing the carry-over effects for soil water and nutrients in crop rotations (Kollas et al, 2015).…”

Section: Model Choice and Descriptionmentioning

confidence: 99%

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Sustainable intensification of crop residue exploitation for bioenergy: Opportunities and challenges

et al. 2019

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Crop residue exploitation for bioenergy can play an important role in climate change mitigation without jeopardizing food security, but it may be constrained by impacts on soil organic carbon (SOC) stocks, and market, logistic and conversion challenges. We explore opportunities to increase bioenergy potentials from residues while reducing environmental impacts, in line with sustainable intensification. Using the case study of North Rhine‐Westphalia in Germany, we employ a spatiotemporally explicit approach combined with stakeholder interviews. First, the interviews identify agronomic and environmental impacts due to the potential reduction in SOC as the most critical challenge associated with enhanced crop residue exploitation. Market and technological challenges and competition with other residue uses are also identified as significant barriers. Second, with the use of agroecosystem modelling and estimations of bioenergy potentials and greenhouse gas emissions till mid‐century, we evaluate the ability of agricultural management to tackle the identified agronomic and environmental challenges. Integrated site‐specific management based on (a) humus balancing, (b) optimized fertilization and (c) winter soil cover performs better than our reference scenario with respect to all investigated variables. At the regional level, we estimate (a) a 5% increase in technical residue potentials and displaced emissions from substituting fossil fuels by bioethanol, (b) an 8% decrease in SOC losses and associated emissions, (c) an 18% decrease in nitrous oxide emissions, (d) a 37% decrease in mineral fertilizer requirements and emissions from their production and (e) a 16% decrease in nitrate leaching. Results are spatially variable and, despite improvements induced by management, limited amounts of crop residues are exploitable for bioenergy in areas prone to SOC decline. In order to sustainably intensify crop residue exploitation for bioenergy and reconcile climate change mitigation with other sustainability objectives, such as those on soil and water quality, residue management needs to be designed in an integrated and site‐specific manner.

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Section: Methodological Approachmentioning

confidence: 99%

Section: Model Choice and Descriptionmentioning

confidence: 99%

Sustainable intensification of crop residue exploitation for bioenergy: Opportunities and challenges

et al. 2019

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“…The performance of the MONICA model showed the capability to adequately reproduce crop yields for different crops (Asseng et al, 2013;Bassu et al, 2014;Kothari et al, 2022;Rötter et al, 2012;Salo et al, 2016) and more complex crop rotations (Kollas et al, 2015;Kostková et al, 2021). For SOC dynamics under bare fallow treatment, MONICA performed similarly to simple C turnover models (Farina et al, 2021) but furthermore demonstrated also good performance when simulating short-term high-resolution CO 2 exchange in a soil-plant system (Specka et al, 2016). The Daisy model, another agroecosystem model, had already been tested against long-term soil carbon experiments (Smith et al, 1997) and performed comparably well to soil carbon turnover models, such as RothC (Coleman and Jenkinson, 1996) or CANDY (Franko et al, 1995).…”

Section: Introductionmentioning

confidence: 91%

Evaluation and optimisation of the soil carbon turnover routine in the MONICA model (version 3.3.1)

Aiteew,

Rouhiainen,

Nendel

et al. 2024

Geosci. Model Dev.

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Abstract. Simulation models are tools commonly used to predict changes in soil carbon stocks. Prior validation is essential, however, for determining the reliability and applicability of model results. In this study, the process-based biogeochemical model MONICA (Model of Nitrogen and Carbon dynamics on Agro-ecosystems) was evaluated with respect to soil organic carbon (SOC), using long-term monitoring data from 46 German agricultural sites. A revision and parameterisation of equations, encompassing crop- and fertiliser-specific C contents and the abiotic factors of soil temperature, soil water and clay content, were undertaken and included in the model. The modified version was also used for a Morris elementary effects screening method, which confirmed the importance of environmental and management factors to the model's performance. The model was then calibrated by means of Bayesian inference, using the Metropolis–Hastings algorithm. The performance of the MONICA model was compared with that of five established carbon turnover models (CCB, CENTURY, C-TOOL, ICBM and RothC). The original MONICA model systematically overestimated SOC decomposition rates and produced on average a ∼17 % greater mean absolute error (MAE) than the other models. The modification and calibration significantly improved its performance, reducing the MAE by ∼30 %. Consequently, MONICA outperformed CENTURY, CCB and C-TOOL, and produced results comparable with ICBM and RothC. Use of the modified model allowed mostly adequate reproduction of site-specific SOC stocks, while the availability of a nitrogen, plant growth and water submodel enhanced its applicability when compared with models that only describe carbon dynamics.

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“…For the SaSCiA-model, the crop-model MONICA (version 2.0, [34]) was selected. MONICA computes daily soil moisture content at 10 cm depth intervals from top to a depth of 2 m. Several studies demonstrated the usability of the model (e.g., [41,42]). MONICA is applicable to freely available data and transferable to regional scale [41].…”

Section: Soil Moisture Modelling (Monica)mentioning

confidence: 99%

Spatially Explicit Soil Compaction Risk Assessment of Arable Soils at Regional Scale: The SaSCiA-Model

et al. 2018

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Soil compaction caused by field traffic is one of the main threats to agricultural landscapes. Compacted soils have a reduced hydraulic conductivity, lower plant growth and increased surface runoff resulting in numerous environmental issues such as increased nutrient leaching and flood risk. Mitigating soil compaction, therefore, is a major goal for a sustainable agriculture and environmental protection. To prevent undesirable effects of field traffic, it is essential to know where and when soil compaction may occur. This study developed a model for soil compaction risk assessment of arable soils at regional scale. A combination of (i) soil, weather, crop type and machinery information; (ii) a soil moisture model and (iii) soil compaction models forms the SaSCiA-model (Spatially explicit Soil Compaction risk Assessment). The SaSCiA-model computes daily maps of soil compaction risk and associated area statistics for varying depths at actual field conditions and for entire regions. Applications with open access data in two different study areas in northern Germany demonstrated the model’s applicability. Soil compaction risks strongly varied in space and time throughout the year. SaSCiA allows a detailed spatio-temporal analysis of soil compaction risk at the regional scale, which exceed those of currently available models. Applying SaSCiA may support farmers, stakeholders and consultants in making decision for a more sustainable agriculture.

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Reproducing CO2 exchange rates of a crop rotation at contrasting terrain positions using two different modelling approaches

Cited by 9 publications

References 74 publications

Sustainable intensification of crop residue exploitation for bioenergy: Opportunities and challenges

Sustainable intensification of crop residue exploitation for bioenergy: Opportunities and challenges

Evaluation and optimisation of the soil carbon turnover routine in the MONICA model (version 3.3.1)

Spatially Explicit Soil Compaction Risk Assessment of Arable Soils at Regional Scale: The SaSCiA-Model

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