Yi-Chen Pao

In Germany each federal state is committed to collect required information on funding, farming practices and land use with an “Integrated Administration and Control System” (IACS) (Deutscher Bundestag 2014).Based on the land parcel identification system (LPIS) as one of the core elements of IACS (European Commission, 2025), georeferenced data along with ancillary data are collected annually since 2005. Mandatory requirements for checks and on-site validations ensure a high data quality which makes IACS data very suitable for research purposes (Leonhardt 2024). Our goal is to create a nation-wide timeseries based on IACS data, that contains detailed information on land use, animal husbandry and farm statistics and can be used for comprehensive land use, soil, agricultural-policy and biodiversity research. Despite this, IACS data remain underused for scientific research due to the following challenges:Data protection: Obtaining and handling IACS data requires a legal agreement between the research project and the respective federal state including Data Usage Agreements.Data heterogeneity: All federal states have unique data processing workflows and historical changes in processing practices resulting in different data-types, -formats, structure, keys, encodings, etc.Data volume: Large storage volume, processing capacities and back-up systems with high security levels are required. Efficiency and data minimization is an important framework for the design of the processing workflows. In this contribution we as user-turned-developers, want to show how we utilize our toolbox of open-source software (Linux, Bash, R, PostgreSQL/PostGIS, Python, GitLab), for a suitable modularized workflow to meet these challenges.The first module is tailored to pre-process the data to its specific federal state qualities. Module two and three contain more general functions to grant machine readability. All data is then processed in a data cleaning workflow and imported into our PostgreSQL/PostGIS database.We use our database for data harmonization by implementing modularized functions to handle different use cases.The resulting harmonized datasets are provided to research teams with data protection clearance for federal state and year respectively. Harmonized tables are versioned as releases, to either grant reproducibility as well as to provide necessary updates.Figure 1 Modularized workflow for IACS data processing towards a nation-wide harmonized timeseriesReproducibly is granted by using script-based procedures that are stored and versioned in GitLab as well as extensive code documentation and automized file-based processing documentation.Our modularization process lays the foundation for sustainable handling of complex administrative agricultural data and is a first step towards a software development approach.LiteratureEuropean Commission (2025): Integrated Administration and Control System (IACS). Online available https://agriculture.ec.europa.eu/common-agricultural-policy/financing-cap/assurance-and-audit/managing-payments_enDeutscher Bundestag (2014): Gesetz über die Verarbeitung von Daten im Rahmen des Integrierten Verwaltungs- und Kontrollsystems nach den unionsrechtlichen Vorschriften für Agrarzahlungen. InVeKoS- Daten-Gesetz - InVeKoSDG, vom 5 (2019). Online available: https://www.gesetze-im-internet.de/invekosdg_2015/Heidi Leonhardt, Maximilian Wesemeyer, Andreas Eder, Silke Hüttel, Tobia Lakes, Henning Schaak, Stefan Seifert, Saskia Wolff (2024): Use cases and scientific potential of land use data from the EU’s Integrated Administration and Control System: A systematic mapping review, Ecological Indicators, Volume 167, ISSN 1470-160X, https://doi.org/10.1016/j.ecolind.2024.112709.

The Integrated Administration and Control System (IACS) is a key instrument of the European Union's (EU) Common Agricultural Policy to monitor agricultural subsidies and support evidence-based policy. IACS provides the most comprehensive EU-wide dataset that combines detailed geospatial data with thematic attributes related to land use, livestock and measures, making it highly valuable for research on agri-environmental policies and agrobiodiversity (Leonhardt, et.al., 2024). In Germany, these data are collected independently by 14 federal states, resulting in substantial heterogeneity across datasets in terms of file format, encoding, data structure and level of completeness. These inconsistencies present major challenges for efficient data management, scientific assessments, reproducibility and the long-term reuse of the data.This contribution presents an ongoing automated framework designed to standardise and validate raw IACS datasets across our data management pipeline, from data collection and harmonisation to data import and long-term management. Our main goal is to reduce redundancy and manual effort in the data quality check process, while enabling scalable and reproducible data quality assurance. The objective is to therefore develop an optimised, non-redundant data check system that captures structural, semantic and geospatial metadata from heterogenous datasets using a single-pass folder scan. To achieve this objective, we focus on the following approaches:Develop an inventory-based data pipeline / architecture: A lightweight inventory object containing metadata for each file in the delivery folderAutomate routine and error – prone data quality scripts: Replace manual checks with modular and reusable automated components from a central inventory systemEnable reproducible execution and reporting: Implement a Quarto based framework (an open-source system for reproducible computational documents combining code, results and narrative) that produces human readable visualisations for technical and non-technical usersOur system leverages a diverse set of programming tools including R, Quarto, Bash, Python and SQL, from data delivery or collection to data management in the database. The approach is based on an inventory-first architecture: a lightweight yet expressive data structure generated from a single scan of raw input folder with different types of data formats. The inventory then captures essential metadata of each file such as file types, attribute schemas, geospatial extents, and identifier patterns (e.g., farm identifier, land parcel identifier). A consolidated framework of all data check scripts then enables all subsequent quality-check modules to operate efficiently without repeated file access. Executing the consolidated framework performs a range of automated data quality checks such as file integrity verification, cross-file joinability analysis, schema consistency assessment, and geospatial coherence analysis.The resulting output in the form of an interactive Quarto dashboard then provides a comprehensive first assessment of the delivered data, where all essential metadata and errors of each file can be derived and inspected in one instance. This workflow not only minimises manual work of checking each file separately and error propagation but also ensures traceable, documented logs.Our results show how implementing such automated data checks considerably accelerates harmonization processes and improves the data management lifecycle.

Crop varieties differing in architectural characteristics (AC) vary in their intra-canopy light distribution. To optimize canopy photosynthesis, we hypothesize that varieties with contrasting AC possess different photosynthetic acclimation strategies (PAS) with respect to photosynthetic nitrogen (N_p) partitioning. We firstly used in silico experiments to test this hypothesis and suggested a trade-off in N_p partitioning between carboxylation and light harvesting to achieve optimal coordination between PAS, AC and growing light environment. Then, two cucumber (Cucumis sativus L.) cultivars, Aramon and SC-50, which were bred under greenhouse vertical single-stem and field creeping multi-branch canopy, were selected for studying their differences in AC and PAS using greenhouse and growth chamber experiments, respectively. In the greenhouse, more horizontal leaves of SC-50 resulted in steeper intra-canopy light gradient and a higher degree of self-shading, especially in the upper canopy layer. In growth chamber experiments, Aramon invested more leaf nitrogen into photosynthesis than SC-50, and the proportion (p_Np) increased as light was reduced. In contrast, p_Np of SC-50 did not respond to light but SC-50 partitioned its limited N_p between carboxylation and light harvesting functions more effectively, showing a strategy particularly advantageous for canopies with a high degree of self-shading. This is further confirmed by additional in silico experiments showing that N_p partitioning of SC-50 coped better with the impact of strong light competition caused by low light and by leaf clumping under high planting density. These findings provide a comprehensive perspective of genotypic variation in photosynthetic acclimation strategies, canopy architectures and their optimal coordination.

One-dimensional light models using the Beer–Lambert equation (BL) with the light extinction coefficient k are simple and robust tools for estimating light interception of homogeneous canopies. Functional–structural plant models (FSPMs) are powerful to capture light–plant interactions in heterogeneous canopies, but they are also more complex due to explicit descriptions of three-dimensional plant architecture and light models. For choosing an appropriate modelling approach, the trade-offs between simplicity and accuracy need to be considered when canopies with spatial heterogeneity are concerned. We compared two light modelling approaches, one following BL and another using ray tracing (RT), based on a framework of a dynamic FSPM of greenhouse cucumber. Resolutions of hourly step (HS) and daily step (DS) were applied to simulate light interception, leaf-level photosynthetic acclimation and plant-level dry matter production over growth periods of 2–5 weeks. Results showed that BL-HS was comparable to RT-HS in predicting shoot dry matter and photosynthetic parameters. The k used in the BL approach was simulated using an empirical relationship between k and leaf area index established with the assistance of RT, which showed variation up to 0.2 in k depending on canopy geometry under the same plant density. When a constant k value was used instead, a difference of 0.2 in k resulted in up to 27 % loss in accuracy for shoot dry matter. These results suggested that, with the assistance of RT in k estimation, the simple approach BL-HS provided efficient estimation for long-term processes.

Pathogenesis-related (PR) proteins are known to play relevant roles in plant defense against biotic and abiotic stresses. In the present study, we characterize the response of transgenic faba bean (Vicia faba L.) plants encoding a PR10a gene from potato (Solanum tuberosum L.) to salinity and drought. The transgene was under the mannopine synthetase (pMAS) promoter. PR10a-overexpressing faba bean plants showed better growth than the wild-type plants after 14 days of drought stress and 30 days of salt stress under hydroponic growth conditions. After removing the stress, the PR10a-plants returned to a normal state, while the wild-type plants could not be restored. Most importantly, there was no phenotypic difference between transgenic and non-transgenic faba bean plants under well-watered conditions. Evaluation of physiological parameters during salt stress showed lower Na⁺-content in the leaves of the transgenic plants, which would reduce the toxic effect. In addition, PR10a-plants were able to maintain vegetative growth and experienced fewer photosystem changes under both stresses and a lower level of osmotic stress injury under salt stress compared to wild-type plants. Taken together, our findings suggest that the PR10a gene from potato plays an important role in abiotic stress tolerance, probably by activation of stress-related physiological processes.

Acclimation of leaf traits to fluctuating environments is a key mechanism to maximize fitness. One of the most important strategies in acclimation to changing light is to maintain efficient utilization of nitrogen in the photosynthetic apparatus by continuous modifications of between-leaf distribution along the canopy depth and within-leaf partitioning between photosynthetic functions according to local light availability. Between-leaf nitrogen distribution has been intensively studied over the last three decades, where proportional coordination between nitrogen concentration and light gradient was considered optimal in terms of maximizing canopy photosynthesis, without taking other canopy structural and physiological factors into account. We proposed a mechanistic model of protein turnover dynamics in different photosynthetic functions, which can be parameterized using leaves grown under different levels of constant light. By integrating this dynamic model into a multi-layer canopy model, constructed using data collected from a greenhouse experiment, it allowed us to test in silico the degree of optimality in photosynthetic nitrogen use for maximizing canopy carbon assimilation under given light environments.

Leaves adapted to diurnal fluctuating light (FL) tend to have reduced photosynthetic parameters in comparison with those grown under constant light but intercepting the same daily photon integral (DPI). This reduction may result from a non-linear relationship between photosynthetic protein synthesis rate (PPSR) and incident photosynthetically active radiation (PAR). Models incorporating the PPSR–PAR relationship have quantitatively predicted the effects of FL reported in the literature. Further simulations suggest that the degree of this reduction varies with the FL pattern, DPI level and parameters describing the PPSR–PAR relationship.

Plants continually adjust the photosynthetic functions in their leaves to fluctuating light, thereby optimizing the use of photosynthetic nitrogen (N_ph) at the canopy level. To investigate the complex interplay between external signals during the acclimation processes, a mechanistic model based on the concept of protein turnover (synthesis and degradation) was proposed and parameterized using cucumber grown under nine combinations of nitrogen and light in growth chambers. Integrating this dynamic model into a multi-layer canopy model provided accurate predictions of photosynthetic acclimation of greenhouse cucumber canopies grown under high and low nitrogen supply in combination with day-to-day fluctuations in light at two different levels. This allowed us to quantify the degree of optimality in canopy nitrogen use for maximizing canopy carbon assimilation, which was influenced by N_ph distribution along canopy depth or N_ph partitioning between functional pools. Our analyses suggest that N_ph distribution is close to optimum and N_ph reallocation is more important under low nitrogen. N_ph partitioning is only optimal under a light level similar to the average light intensity during acclimation, meaning that day-to-day light fluctuations inevitably result in suboptimal N_ph partitioning. Our results provide insights into photoacclimation and can be applied to crop model improvement.