Newest publications
High-performance thin-layer chromatography for the detection of compositional changes in LACTEM emulsifiers during storage
(2025) Schuster, Katharina; Blankart, Max; Hinrichs, Jörg; Oellig, Claudia
Quality control of food emulsifiers, such as lactic acid esters of mono- and diacylglycerols (LACTEM), is crucial in the reproducible production of food products. The current study investigated compositional changes of LACTEM emulsifiers using high-performance thin-layer chromatography (HPTLC) during storage at 60 °C for 8 weeks. Ultraviolet (UV) and fluorescence images of the HPTLC silica gel F254s plates after primuline derivatization and densitometric data were analyzed to assess changes in the composition. Significant changes were observed for minor LACTEM components (< 10% relative intensity), specifically a decrease in higher-lactylated monoacylglycerols and an increase in triacylglycerols. Techno-functional properties, such as particle size distribution, apparent viscosity, overrun, foam firmness, drainage, and residual cream of aerosol whipping cream (0.8 g 100 g−1 LACTEM) were investigated. While emulsion stability was not affected, the foam firmness increased significantly, corresponding to a visibly more brittle foam. On the basis of these results, monitoring compositional changes in the food-manufacturing process is necessary to maintain constant food quality.
Effects of using deep learning to predict the geographic origin of barley genebank accessions on genome–environment association studies
(2025) Chang, Che-Wei; Schmid, Karl
Genome–environment association (GEA) is an approach for identifying adaptive loci by combining genetic variation with environmental parameters, offering potential for improving crop resilience. However, its application to genebank accessions is limited by missing geographic origin data. To address this limitation, we explored the use of neural networks to predict the geographic origins of barley accessions and integrate imputed environmental data into GEA. Neural networks demonstrated high accuracy in cross-validation but occasionally produced ecologically implausible predictions as models solely considered geographical proximity. For example, some predicted origins were located within non-arable regions, such as the Mediterranean Sea. Using barley flowering time genes as benchmarks, GEA integrating imputed environmental data ( N=11,032) displayed partially concordant yet complementary detection of genomic regions near flowering time genes compared to regular GEA ( N=1,626), highlighting the potential of GEA with imputed data to complement regular GEA in uncovering novel adaptive loci. Also, contrary to our initial hypothesis anticipating a significant improvement in GEA performance by increasing sample size, our simulations yield unexpected insights. Our study suggests potential limitations in the sensitivity of GEA approaches to the considerable expansion in sample size achieved through predicting missing geographical data. Overall, our study provides insights into leveraging incomplete geographical origin data by integrating deep learning with GEA. Our findings indicate the need for further development of GEA approaches to optimize the use of imputed environmental data, such as incorporating regional GEA patterns instead of solely focusing on global associations between allele frequencies and environmental gradients across large-scale landscapes.
Monitoring soil carbon in smallholder carbon projects: insights from Kenya
(2024) Okoli, Adaugo O.; Birkenberg, Athena; Okoli, Adaugo O.; Department of Social and Institutional Change in Agricultural Development, Hans-Ruthenberg-Institute of Agricultural Science in the Tropics, University of Hohenheim, Wollgrasweg 43, 70599, Stuttgart, Germany; Birkenberg, Athena; Department of Social and Institutional Change in Agricultural Development, Hans-Ruthenberg-Institute of Agricultural Science in the Tropics, University of Hohenheim, Wollgrasweg 43, 70599, Stuttgart, Germany
Voluntary carbon market schemes facilitate funding for projects promoting sustainable land management practices to sequester carbon in natural sinks such as biomass and soil, while also supporting agricultural production. The effectiveness of VCM schemes relies on accurate measurement mechanisms that can directly attribute carbon accumulation to project activities. However, measuring carbon sequestration in soils has proven to be difficult and costly, especially in fragmented smallholdings predominant in global agriculture. The cost and accuracy limitations of current methods to monitor soil organic carbon (SOC) limit the participation of smallholder farmers in global carbon markets, where they could potentially be compensated for adopting sustainable farming practices that provide ecosystem benefits. This study evaluates nine different approaches for SOC accounting in smallholder agricultural projects. The approaches involve the use of proximal and remote sensing, along with process models. Our evaluation centres on stakeholder requirements for the Measurement, Reporting, and Verification system, using the criteria of accuracy, level of standardisation, costs, adoptability, and the advancement of community benefits. By analysing these criteria, we highlight opportunities and challenges associated with each approach, presenting suggestions to enhance their applicability for smallholder SOC accounting. The contextual foundation of the research is a case study on the Western Kenya Soil Carbon Project. Remote sensing shows promise in reducing costs for direct and modelling-based carbon measurement. While it is already being used in certain carbon market applications, transparency is vital for broader integration. This demands collaborative work and investment in infrastructure like spectral libraries and user-friendly tools. Balancing community benefits against the detached nature of remote techniques is essential. Enhancing information access aids farmers, boosting income through improved soil and crop productivity, even with remote monitoring. Handheld sensors can involve smallholders, given consistent protocols. Engaging the community in monitoring can cut project costs, enhance agricultural capabilities, and generate extra income.
Correction to: Breeding progress of nitrogen use efficiency of cereal crops, winter oilseed rape and peas in long-term variety trials
(2024) Laidig, Friedrich; Feike, T.; Lichthardt, C.; Schierholt, A.; Piepho, Hans-Peter; Laidig, F.; Institute of Crop Science, Biostatistics Unit, University of Hohenheim, Fruwirthstrasse 23, 70599, Stuttgart, Germany; Feike, T.; Julius Kühn Institute – Federal Research Centre for Cultivated Plants, Institute for Strategies and Technology Assessment, Stahnsdorfer Damm 81, 14532, Kleinmachnow, Germany; Lichthardt, C.; Bundessortenamt, Osterfelddamm 60, 30627, Hannover, Germany; Schierholt, A.; Plant Breeding Methodology, Georg-August-University Göttingen, Carl-Sprengel-Weg 1, 37075, Göttingen, Germany; Piepho, H. P.; Institute of Crop Science, Biostatistics Unit, University of Hohenheim, Fruwirthstrasse 23, 70599, Stuttgart, Germany
Breeding progress of nitrogen use efficiency of cereal crops, winter oilseed rape and peas in long-term variety trials
(2024) Laidig, Friedrich; Feike, T.; Lichthardt, C.; Schierholt, A.; Piepho, Hans-Peter; Laidig, F.; Institute of Crop Science, Biostatistics Unit, University of Hohenheim, Fruwirthstrasse 23, 70599, Stuttgart, Germany; Feike, T.; Julius Kühn Institute – Federal Research Centre for Cultivated Plants, Institute for Strategies and Technology Assessment, Stahnsdorfer Damm 81, 14532, Kleinmachnow, Germany; Lichthardt, C.; Bundessortenamt, Osterfelddamm 60, 30627, Hannover, Germany; Schierholt, A.; Plant Breeding Methodology, Georg-August-University Göttingen, Carl-Sprengel-Weg 1, 37075, Göttingen, Germany; Piepho, H. P.; Institute of Crop Science, Biostatistics Unit, University of Hohenheim, Fruwirthstrasse 23, 70599, Stuttgart, Germany
Breeding and registration of improved varieties with high yield, processing quality, disease resistance and nitrogen use efficiency (NUE) are of utmost importance for sustainable crop production to minimize adverse environmental impact and contribute to food security. Based on long-term variety trials of cereals, winter oilseed rape and grain peas tested across a wide range of environmental conditions in Germany, we quantified long-term breeding progress for NUE and related traits. We estimated the genotypic, environmental and genotype-by-environment interaction variation and correlation between traits and derived heritability coefficients. Nitrogen fertilizer application was considerably reduced between 1995 and 2021 in the range of 5.4% for winter wheat and 28.9% for spring wheat while for spring barley it was increased by 20.9%. Despite the apparent nitrogen reduction for most crops, grain yield (GYLD) and nitrogen accumulation in grain (NYLD) was increased or did not significantly decrease. NUE for GYLD increased significantly for all crops between 12.8% and 35.2% and for NYLD between 8% and 20.7%. We further showed that the genotypic rank of varieties for GYLD and NYLD was about equivalent to the genotypic rank of the corresponding traits of NUE, if all varieties in a trial were treated with the same nitrogen rate. Heritability of nitrogen yield was about the same as that of grain yield, suggesting that nitrogen yield should be considered as an additional criterion for variety testing to increase NUE and reduce negative environmental impact.