Browsing by Subject "Model coupling"

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Assessing impacts of crop area expansion and crop-livestock integration on ecosystem functions in African savannas using the coupled LUCIA and LIVSIM models
(2025) Gutai, Benjamin; Marohn, Carsten; Bateki, Christian Adjogo; Asch, Folkard; Gutai, Benjamin; Institute of Agricultural Sciences in the Tropics (Hans-Ruthenberg-Institute), University of Hohenheim, Garbenstr. 13, 70599, Stuttgart, Germany; Marohn, Carsten; Institute of Agricultural Sciences in the Tropics (Hans-Ruthenberg-Institute), University of Hohenheim, Garbenstr. 13, 70599, Stuttgart, Germany; Bateki, Christian Adjogo; Section Animal Husbandry in the Tropics and Subtropics, University of Kassel and University of Göttingen, Steinstr. 19, 37213, Witzenhausen, Germany; Asch, Folkard; Institute of Agricultural Sciences in the Tropics (Hans-Ruthenberg-Institute), University of Hohenheim, Garbenstr. 13, 70599, Stuttgart, Germany
Large-scale land use change (LUC) of African Guinea savannas to crop fields is expected to cause negative impacts on ecosystem functions (ESF) and long term land productivity. The complex interactions of key processes in savannas evoked by LUC calls for a process-based modelling approach. We employed the dynamically coupled Land Use Change Impact Assessment (LUCIA) model and the Livestock Simulator (LIVSIM) which represent LUC impacts on soil processes, landscape-scale matter fluxes, seasonal grass and crop growth, and livestock nutrition, production and reproduction, depending on seasonal feed availability and quality on accessible pastures. For a rangeland in Borana, Ethiopia, two different LUC scenarios were evaluated in comparison to the baseline of traditional pasture-based land use. In the intensive LUC scenario 52% of grassland was converted into unfertilized maize fields, inaccessible for livestock. The integrated LUC scenario of the same grassland conversion rate allowed feeding maize straw and provided high-quality feed reserves from seasonally managed pastures. LUC in the intensive LUC scenario led to declining yields in the second year after conversion. Feed production on the remaining rangeland patches was insufficient for livestock nutrition, causing drops of herd body weight and herd size particularly in drought years. Resilience of herd performance to LUC was enhanced in the integrated LUC scenario when feeding maize straw and high-quality feed reserves. In both LUC scenarios, topsoil organic carbon storage decreased after ploughing shrub grassland for cultivation, and so did soil water storage capacity due to soil pore destruction. Soil erosion of less than one cm after 10 years occurred under cultivation. The simulation results indicated that the well validated model framework could predict impacts of LUC and simple crop-livestock integration on savanna ESFs, grass growth dynamics and livestock production during seasonal and inter-annual rainfall variation. This study lays the foundation for further land use scenario simulations to improve the understanding of benefits and risks caused by savanna grassland conversion.
Implications of large‐scale miscanthus cultivation in water protection areas: A Life Cycle Assessment with model coupling for improved policy support
(2022) Weik, Jan; Lask, Jan; Petig, Eckart; Seeger, Stefan; Marting Vidaurre, Nirvana; Wagner, Moritz; Weiler, Markus; Bahrs, Enno; Lewandowski, Iris; Angenendt, Elisabeth
Two major global challenges related to agriculture are climate change and the unbalanced nitrogen cycle. For both, national and international reduction targets have been defined to catalyse policy support for more sustainable farming systems. Miscanthus cultivation in water protection areas has been proposed as a contribution to achieving these targets. However, a thorough understanding of the underlying system dynamics at various spatial levels is required before recommendations for policy development can be provided. In this study, a model framework was established to provide economic and environmental indicator results at regional and sub‐regional levels. It presents a consequential Life Cycle Assessment coupled with an agro‐economic supply model (Economic Farm Emission Model) that simulates crop and livestock production, and an agricultural hydrology model (DAISY) that assesses effects on the nitrogen cycle. The framework is applied to Baden‐Württemberg, a federal state in southwest Germany with eight agro‐ecological regions. Scenarios investigating the differences between mandatory and voluntary miscanthus cultivation were also explored. While the results show the high potential of miscanthus cultivation for the reduction of greenhouse gas emissions (−16% to −724%), the potential to reduce nitrate leaching (−4% to −44%) is compromised in some sub‐regions and scenarios (+4% to +13%) by substantial effects on the crop rotation. Furthermore, the cultivation of miscanthus reduces gross margins in most sub‐regions (−0.1% to −9.6%) and decreases domestic food production (−1% to −50%). However, in regions with low livestock density and high yields, miscanthus cultivation can maintain or increase farmers' income (0.1%–5.8%) and improve environmental protection. The study shows that the heterogeneity of arable land requires a flexible promotion programme for miscanthus. Voluntary cultivation schemes were identified as most suitable to capture sub‐regional differences. Policies should address the demand for miscanthus, for example, support the development of regional value chains, including farmers, water suppliers and the biobased industry.
Linking farm economics and hydrology: Model integration for watershed-level irrigation management applied to Chile
(2010) Arnold, Thorsten; Berger, Thomas
As largest user of fresh water, the agricultural sector must resolve conflict of objectives ranging from economic goals of farmers to societal and environmental targets. Research must deliver tools to manage these objectives simultaneously. Single disciplines have resolved numerous problems with disciplinary solutions. However, problems emerging from interactions and feedbacks between disciplines can only be assessed with interdisciplinary tools and managed by institutions that coordinate across departments. Such complex problems are becoming an epochal task for Natural Resource Management (NRM). A number of modeling tools exist for irrigation management at watershed level that quantify biophysical processes and water quality. Simultaneously, agricultural economics developed production planning methods for allocating water resources optimally. However, integrated planning support tools are not available that take into account both domains and their interactions. Within a larger research project, it was the objective of this Ph.D. project to develop and test methods that integrate two complex modelling softwares for irrigation management. The distributed runoff model WaSiM-ETH quantifies water flows and evapotranspiration. The dynamic land use model MP-MAS is a multi-agent system in which farmers use economic reasoning to derive cropping decisions under given environmental conditions. Furthermore, the MP-MAS software contains the bucket model EDIC, which parameterizes the distribution of water from rivers to individual farmers through the canal system. Finally, the MP-MAS software was extended with a crop yield model with complementary irrigation. Model integration is understood as service provided within a research context. This context is defined by the study region, the project setting and by the strategic decisions within the research project - such as the choice of partner institutions and disciplines. Within the Maule River watershed in Chile (Linares Province, Region VII), the project ?Integrating Governance and Modeling? assessed the use of water in agriculture. Empirical research questions as well as modeling software were also part of this research context. Integration requires the conceptual, the technical and the procedural level. Conceptual integration describes processes and interactions between farmers, the canal system as distribution infrastructure and the natural system. It also describes how farmers plan and produce within this environment. Here, scale-dependent processes like irrigation efficiency or access to water by individuals were scrutinized. Technical integration is the implementation of the conceptual system into source code, e.g. by adapting legacy software, and by creating a software layer for hierarchical coupling of all software components. Procedural integration is the calibration, analysis, error eradication and validation of these models within the research context. Calibration and analysis of integrated model components is a step-by-step procedure. For all relevant processes and interactions, empirical data was first compiled and cross-evaluated. Then, standalone model components were calibrated so that interactions were parameterized as boundary conditions that are consistent across all disciplines. Empirical data pinpointed conceptual inconsistencies in the description of interactions, and standalone models were improved together with project partners. Ultimately, model components were coupled in such ways that interactions can be analyzed dynamically at minimum model- and software complexity. The calibration process along transdisciplinary cause-effect-chains resulted in the improvement of disciplinary models and model results. For example, the relevance of access to water beyond legalized water rights became apparent when empirical data and models were combined. Also, the calibration of the EDIC model required consistent use of data from all four disciplines and improved the calibration of the MP-MAS model. For the WaSiM-ETH model, an irrigation module was developed that is consistent across scales and reflects the needs of extension workers. Finally, model integration and coupling is discussed as research process. The process of calibrating a model with four components is not only a technical challenge for modellers and data management, but also a procedural challenge with regards to cooperation beyond disciplinary institutions and cultures. The structure of the integration process should be robust against errors and equally facilitate knowledge transfer between disciplines, iterative calibration across disciplines. Success factors are suggested to reduce transaction cost during the integration process.