Flavian Tschurr

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Tschurr

First Name

Flavian

ORCID

0000-0001-7986-1556

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03648 - Buchmann, Nina / Buchmann, Nina

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Publications 1 - 10 of 10

From Neglecting to Including Cultivar-Specific Per Se Temperature Responses: Extending the Concept of Thermal Time in Field Crops
Roth, Lukas; Binder, Martina; Kirchgessner, Norbert; et al. (2024)
Plant Phenomics
Predicting plant development, a longstanding goal in plant physiology, involves 2 interwoven components: continuous growth and the progression of growth stages (phenology). Current models for winter wheat and soybean assume species-level growth responses to temperature. We challenge this assumption, suggesting that cultivar-specific temperature responses substantially affect phenology. To investigate, we collected field-based growth and phenology data in winter wheat and soybean over multiple years. We used diverse models, from linear to neural networks, to assess growth responses to temperature at various trait and covariate levels. Cultivar-specific nonlinear models best explained phenology-related cultivar-environment interactions. With cultivar-specific models, additional relations to other stressors than temperature were found. The availability of the presented field phenotyping tools allows incorporating cultivar-specific temperature response functions in future plant physiology studies, which will deepen our understanding of key factors that influence plant development. Consequently, this work has implications for crop breeding and cultivation under adverse climatic conditions.
Frost Damage Index: The Antipode of Growing Degree Days
Tschurr, Flavian; Kirchgessner, Norbert; Hund, Andreas; et al. (2023)
Plant Phenomics
Abiotic stresses such as heat and frost limit plant growth and productivity. Image-based field phenotyping methods allow quantifying not only plant growth but also plant senescence. Winter crops show senescence caused by cold spells, visible as declines in leaf area. We accurately quantified such declines by monitoring changes in canopy cover based on time-resolved high-resolution imagery in the field. Thirty-six winter wheat genotypes were measured in multiple years. A concept termed “frost damage index” (FDI) was developed that, in analogy to growing degree days, summarizes frost events in a cumulative way. The measured sensitivity of genotypes to the FDI correlated with visual scorings commonly used in breeding to assess winter hardiness. The FDI concept could be adapted to other factors such as drought or heat stress. While commonly not considered in plant growth modeling, integrating such degradation processes may be key to improving the prediction of plant performance for future climate scenarios.
Temporal resolution trumps spectral resolution in UAV-based monitoring of cereal senescence dynamics
Tschurr, Flavian; Roth, Lukas; Storni, Nicola; et al. (2024)
Plant Methods
Background: Senescence is a complex developmental process that is regulated by a multitude of environmental, genetic, and physiological factors. Optimizing the timing and dynamics of this process has the potential to significantly impact crop adaptation to future climates and for maintaining grain yield and quality, particularly under terminal stress. Accurately capturing the dynamics of senescence and isolating the genetic variance component requires frequent assessment as well as intense field testing. Here, we evaluated and compared the potential of temporally dense drone-based RGB- and multispectral image sequences for this purpose. Regular measurements were made throughout the grain filling phase for more than 600 winter wheat genotypes across three experiments in a high-yielding environment of temperate Europe. At the plot level, multispectral and RGB indices were extracted, and time series were modelled using different parametric and semi-parametric models. The capability of these approaches to track senescence was evaluated based on estimated model parameters, with corresponding parameters derived from repeated visual scorings as a reference. This approach represents the need for remote-sensing based proxies that capture the entire process, from the onset to the conclusion of senescence, as well as the rate of the progression. Results: Our results indicated the efficacy of both RGB and multispectral reflectance indices in monitoring senescence dynamics and accurately identifying key temporal parameters characterizing this phase, comparable to more sophisticated proximal sensing techniques that offer limited throughput. Correlation coefficients of up to 0.8 were observed between multispectral (NDVIred668-index) and visual scoring, respectively 0.9 between RGB (ExGR-index) and visual scoring. Sub-sampling of measurement events demonstrated that the timing and frequency of measurements were highly influential, arguably even more than the choice of sensor. Conclusions: Remote-sensing based proxies derived from both RGB and multispectral sensors can capture the senescence process accurately. The sub-sampling emphasized the importance of timely and frequent assessments, but also highlighted the need for robust methods that enable such frequent assessments to be made under variable environmental conditions. The proposed measurement and data processing strategies can improve the measurement and understanding of senescence dynamics, facilitating adaptive crop breeding strategies in the context of climate change.
Crop Phenology in a Changing Climate: Linking Phenotyping Methods, Crop Models, and Climate Scenarios
Tschurr, Flavian (2024)
Mixing things up! Identifying early diversity benefits and facilitating the development of improved variety mixtures with high throughput field phenotyping
Tschurr, Flavian; Oppliger, Corina; Wuest, Samuel E.; et al. (2023)
The Plant Phenome Journal
Crop diversification is a potential strategy to increase the stability and productivity of crops, while reducing pathogen pressures and pesticide requirements. Crop variety mixtures provide some of these diversification benefits and their cultivation is fully compatible with current mechanized agronomic practices. However, the development of optimal variety mixtures is a long, labour-intense process requiring extensive field trials. High throughput field phenotyping (HTFP) methods provide promising applications in field testing because they allow for precise, repeatable, and rapid measurements of crop properties. Here, we evaluated the use of HTFP for developing high-performing oat (Avena sativa) variety mixtures by testing its suitability to predict diversity yield benefits from repeated canopy measurements across the growing season. Analyzing 26 mixtures of five varieties, we found significant overyielding at harvest, that is, mixtures were on average more productive than expected based on component pure stands. This grain yield overyielding was well predicted from deviations between mixture and pure stand canopy cover estimations, derived from HTFP mid-way through the growing season. This shows that (i) positive interactions between oat varieties occur already at an early stage, (ii) such interactions lead to increased potential for light interception, (iii) HTFP offers rapid, scalable methods to screen for performant variety mixtures.
Pixel to practice: multi-scale image data for calibrating remote-sensing-based winter wheat monitoring methods
Anderegg, Jonas; Tschurr, Flavian; Kirchgessner, Norbert; et al. (2024)
Scientific Data
Site-specific crop management in heterogeneous fields has emerged as a promising avenue towards increasing agricultural productivity whilst safeguarding the environment. However, successful implementation is hampered by insufficient availability of accurate spatial information on crop growth, vigor, and health status at large scales. Challenges persist particularly in interpreting remote sensing signals within commercial crop production due to the variability in canopy appearance resulting from diverse factors. Recently, high-resolution imagery captured from unmanned aerial vehicles has shown significant potential for calibrating and validating methods for remote sensing signal interpretation. We present a comprehensive multi-scale image dataset encompassing 35,000 high-resolution aerial RGB images, ground-based imagery, and Sentinel-2 satellite data from nine on-farm wheat fields in Switzerland. We provide geo-referenced orthomosaics, digital elevation models, and shapefiles, enabling detailed analysis of field characteristics across the growing season. In combination with rich meta data such as detailed records of crop husbandry, crop phenology, and yield maps, this data set enables key challenges in remote sensing-based trait estimation and precision agriculture to be addressed.
On-farm evaluation of UAV-based aerial imagery for season-long weed monitoring under contrasting management and pedoclimatic conditions in wheat
Anderegg, Jonas; Tschurr, Flavian; Kirchgessner, Norbert; et al. (2023)
Computers and Electronics in Agriculture
Timely availability of weed infestation maps is a key prerequisite for the implementation of site-specific weed management practices. Low-altitude aerial imagery obtained from unmanned aerial vehicles (UAVs) has shown significant potential for weed detection in crops. However, most studies focused on wide-spaced row crops such as maize and sunflower and evaluated proposed methods on single or few well-characterized experimental sites at specific points in time representing a limited range of application scenarios. This study evaluated the feasibility of weed detection in on-farm wheat fields characterized by a narrow row spacing, throughout the early and late developmental stages using UAV imagery and ground-based high-resolution imagery. Image data was obtained for nine sites, representing a wide range of management and pedoclimatic conditions. These sites can be seen as a representative sample of scenarios that would be encountered in practice. A high within- and across-site as well as temporal variation was observed for weed infestation levels and weed population species composition, highlighting the need for spatially and temporally resolved weed mapping. Image-based classification of vegetation objects as crop or weed plants was achieved with an accuracy of 0.88 and 0.72 in ground-based high-resolution images and UAV-based aerial images captured from an altitude of 10 m, respectively. The accuracy of pixel-wise, vegetation-index-based weed infestation estimation during the late vegetative stages varied strongly across sites. Our results highlight the critical importance of a high ground resolution for weed detection using object-based image analysis during the critical growth stages of wheat and of robust methods that are applicable across a range of scenarios. This suggested that future research aiming at a rapid implementation of site-specific weed management in wheat should focus on the development of ground-based systems. Yet, aerial monitoring of wheat stands during late developmental stages using currently available equipment offers significant potential for reducing weed pressure with site-specific weed control measures in the context of crop rotations.
DyMEP: R package for weather data-based phenology prediction for ten crops
Tschurr, Flavian; Walter, Achim; Roth, Lukas (2025)
Computers and Electronics in Agriculture
For most digital agriculture applications, such as in-season yield predictions, information on crop phenology is a prerequisite. Phenology is largely determined by environmental factors, e.g., temperature, precipitation, and global radiation. Consequently, weather data can be used to predict phenology. Here, we introduce the R package Dynamic Multi-Environmental Phenology (DyMEP) that facilitates such predictions. DyMEP was trained for ten crops, among others winter wheat, spring wheat, barley, green peas, beans and oat, with a large dataset representing the Central European climate. DyMEP fills the gap between complex, highly parameterized crop growth models that are difficult to use by non-experts, and extremely simplified models such as the Growing Degree Day approach. By carefully selecting the environmental covariates to use for each phenological phase, the user can reach suitable prediction accuracy for most applications in DyMEP. If temperature, precipitation, relative humidity, and global radiation are available as covariates to select from, the package achieves absolute errors ranging from 0 to 6 days across all applied phenology phases and root mean square errors ranging from 7 to 17 days on an independent test set. Combining DyMEP-based phenology predictions with ground-based or remote sensing observations holds promise to facilitate digital agriculture applications such as large-scale yield forecasting or monitoring of fields for crop insurance.
Enhanced gap-filling for satellite-derived crop monitoring using temperature-driven reconstruction techniques
Tschurr, Flavian; Graf, Lukas Valentin; Walter, Achim; et al. (2025)
Smart Agricultural Technology
A solid understanding of plant growth is crucial for maintaining future crop productivity in the face of climate change. Remote sensing of crop functional traits using optical satellite imagery provides a valuable tool for determining effective management techniques that reduce risk and enhance agroecosystem resilience. However, atmospheric disturbances limit the availability of imagery, leading to gaps and noise in the trait time series. Thus, accurate crop growth modelling necessitates time series reconstruction methods. One promising approach is incorporating physiological priors, such as the influence of environmental variables like air temperature on plant growth. We propose a novel method that combines Sentinel-2 Green Leaf Area Index (GLAI) observations with three temperature driven reconstruction techniques that describe the physiological relationship between growth and temperature in winter wheat. By employing a probabilistic ensemble Kalman filtering data assimilation scheme, we can integrate high-resolution air temperature data and satellite imagery while quantifying uncertainties. Our results suggest that assimilating Sentinel-2 GLAI and temperature-response-based growth rates allows for the reconstruction of physiologically meaningful GLAI time series. Moreover, our proposed method outperforms state-of-the-art approaches based on logistic functions in terms of physiological plausibility, fitting requirements, and representation of high GLAI values. Our approach requires fewer satellite observations compared to traditional remote sensing time series algorithms, making it suitable for agricultural areas with high cloud cover. Consequently, it has significant potential to improve the reliability of crop productivity assessments based on remote sensing data.
The FIP 1.0 Data Set: Highly resolved annotated image time series of 4,000 wheat plots grown in 6 years
Roth, Lukas; Boss, Mike; Kirchgessner, Norbert; et al. (2025)
GigaScience
Background: Understanding genotype-environment interactions of plants is crucial for crop improvement, yet limited by the scarcity of quality phenotyping data. This Data Note presents the Field Phenotyping Platform 1.0 data set, a comprehensive resource for winter wheat research that combines imaging, trait, environmental, and genetic data. Findings: We provide time-series data for more than 4,000 wheat plots, including aligned high-resolution image sequences totaling more than 153,000 aligned images across 6 years. Measurement data for 8 key wheat traits are included-namely, canopy cover values, plant heights, wheat head counts, senescence ratings, heading date, final plant height, grain yield, and protein content. Genetic marker information and environmental data complement the time series. Data quality is demonstrated through heritability analyses and genomic prediction models, achieving accuracies aligned with previous research. Conclusions: This extensive data set offers opportunities for advancing crop modeling and phenotyping techniques, enabling researchers to develop novel approaches for understanding genotype-environment interactions, analyzing growth dynamics, and predicting crop performance. By making this resource publicly available, we aim to accelerate research in climate-adaptive agriculture and foster collaboration between plant science and machine learning communities.

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Flavian Tschurr

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