Institut für Kulturpflanzenwissenschaften

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Regression approaches for modeling genotype-environment interaction and making predictions into unseen environments
(2026) Hrachov, Maksym; Piepho, Hans-Peter; Rahman, Niaz Md. Farhat; Malik, Waqas Ahmed; Hrachov, Maksym; Biostatistics Unit, Institute of Crop Science, University of Hohenheim, 70593, Stuttgart, Germany; Piepho, Hans-Peter; Biostatistics Unit, Institute of Crop Science, University of Hohenheim, 70593, Stuttgart, Germany; Rahman, Niaz Md. Farhat; Bangladesh Rice Research Institute (BRRI), Gazipur, Bangladesh; Malik, Waqas Ahmed; Biostatistics Unit, Institute of Crop Science, University of Hohenheim, 70593, Stuttgart, Germany
In plant breeding and variety testing, there is an increasing interest in making use of environmental information to enhance predictions for new environments. Here, we will review linear mixed models that have been proposed for this purpose. The emphasis will be on predictions and on methods to assess the uncertainty of predictions for new environments. Our point of departure is straight-line regression, which may be extended to multiple environmental covariates and genotype-specific responses. When observable environmental covariates are used, this is also known as factorial regression. Early work along these lines can be traced back to Stringfield & Salter (1934) and Yates & Cochran (1938), who proposed a method nowadays best known as Finlay-Wilkinson regression. This method, in turn, has close ties with regression on latent environmental covariates and factor-analytic variance-covariance structures for genotype-environment interaction. Extensions of these approaches – reduced rank regression, kernel- or kinship-based approaches, random coefficient regression, and extended Finlay-Wilkinson regression – will be the focus of this paper. Our objective is to demonstrate how seemingly disparate methods are very closely linked and fall within a common model-based prediction framework. The framework considers environments as random throughout, with genotypes also modeled as random in most cases. We will discuss options for assessing uncertainty of predictions, including cross validation and model-based estimates of uncertainty, the latter one being estimated using our new suggested approach. The methods are illustrated using a long-term rice variety trial dataset from Bangladesh.
Spotlight on agroecological cropping practices to improve the resilience of farming systems: a qualitative review of meta-analytic studies
(2025) von Cossel, Moritz; Scordia, Danilo; Altieri, Miguel; Gresta, Fabio
The capacity of agriculture to withstand or recover from increasing stresses (i.e., resilience) will be continuously challenged by extreme climate change events in the coming decades, altering the growing conditions for crop species. By prioritizing natural processes, agroecology seeks to foster climate change adaptation, boost resilience, and contribute to a low-emission agricultural system. Nineteen different agroecological practices using resilience-related terms and “meta-analysis”, within the subject areas ‘Agriculture and Biological Science’ and ‘Environmental Science’ were addressed, and 34 meta-analyses were reviewed to summarize the state-of-the-art agroecological adaptative strategies applied globally, and the current knowledge gaps on the role of agroecological practices in improving farming system resilience. Two main agroecological strategies stand out: i) crop diversification and ii) ecological soil management. The most frequent diversification practices included agroforestry, intercropping, cover cropping, crop rotation, mixed cropping, mixed farming, and the use of local varieties. Soil management practices included green manure, no-till farming, mulching, and the addition of organic matter. The analyzed studies highlight the complex interplay among soil, plant, climate, management, and socio-economic contexts within the selected agroecological practices. The results varied—positive, null, or negative—depending largely on site-specific factors. Developing and understanding more complex systems in a holistic approach, that integrates plants and animals across multiple trophic levels (feeding relationships, nutrient cycling, and aligning with the principles of a circular economy) is essential. More research is, therefore, needed to understand the interactions between crop diversity and soil management, their impacts on resilience, and how to translate research into practical strategies that farmers can implement effectively.
Expanding the BonnMu sequence‐indexed repository of transposon induced maize (Zea mays L.) mutations in dent and flint germplasm
(2024) Win, Yan Naing; Stöcker, Tyll; Du, Xuelian; Brox, Alexa; Pitz, Marion; Klaus, Alina; Piepho, Hans‐Peter; Schoof, Heiko; Hochholdinger, Frank; Marcon, Caroline
The BonnMu resource is a transposon tagged mutant collection designed for functional genomics studies in maize. To expand this resource, we crossed an active Mutator (Mu) stock with dent (B73, Co125) and flint (DK105, EP1, and F7) germplasm, resulting in the generation of 8064 mutagenized BonnMu F2‐families. Sequencing of these Mu‐tagged families revealed 425 924 presumptive heritable Mu insertions affecting 36 612 (83%) of the 44 303 high‐confidence gene models of maize (B73v5). On average, we observed 12 Mu insertions per gene (425 924 total insertions/36 612 affected genes) and 53 insertions per BonnMu F2‐family (425 924 total insertions/8064 families). Mu insertions and photos of seedling phenotypes from segregating BonnMu F2‐families can be accessed through the Maize Genetics and Genomics Database (MaizeGDB). Downstream examination via the automated Mutant‐seq Workflow Utility (MuWU) identified 94% of the presumptive germinal insertion sites in genic regions and only a small fraction of 6% inserting in non‐coding intergenic sequences of the genome. Consistently, Mu insertions aligned with gene‐dense chromosomal arms. In total, 42% of all BonnMu insertions were located in the 5′ untranslated region of genes, corresponding to accessible chromatin. Furthermore, for 38% of the insertions (163 843 of 425 924 total insertions) Mu1, Mu8 and MuDR were confirmed to be the causal Mu elements. Our publicly accessible European BonnMu resource has archived insertions covering two major germplasm groups, thus facilitating both forward and reverse genetics studies.
Genomic prediction using machine learning: a comparison of the performance of regularized regression, ensemble, instance-based and deep learning methods on synthetic and empirical data
(2024) Lourenço, Vanda M.; Ogutu, Joseph O.; Rodrigues, Rui A.P.; Posekany, Alexandra; Piepho, Hans-Peter
Background: The accurate prediction of genomic breeding values is central to genomic selection in both plant and animal breeding studies. Genomic prediction involves the use of thousands of molecular markers spanning the entire genome and therefore requires methods able to efficiently handle high dimensional data. Not surprisingly, machine learning methods are becoming widely advocated for and used in genomic prediction studies. These methods encompass different groups of supervised and unsupervised learning methods. Although several studies have compared the predictive performances of individual methods, studies comparing the predictive performance of different groups of methods are rare. However, such studies are crucial for identifying (i) groups of methods with superior genomic predictive performance and assessing (ii) the merits and demerits of such groups of methods relative to each other and to the established classical methods. Here, we comparatively evaluate the genomic predictive performance and informally assess the computational cost of several groups of supervised machine learning methods, specifically, regularized regression methods, deep , ensemble and instance-based learning algorithms, using one simulated animal breeding dataset and three empirical maize breeding datasets obtained from a commercial breeding program. Results: Our results show that the relative predictive performance and computational expense of the groups of machine learning methods depend upon both the data and target traits and that for classical regularized methods, increasing model complexity can incur huge computational costs but does not necessarily always improve predictive accuracy. Thus, despite their greater complexity and computational burden, neither the adaptive nor the group regularized methods clearly improved upon the results of their simple regularized counterparts. This rules out selection of one procedure among machine learning methods for routine use in genomic prediction. The results also show that, because of their competitive predictive performance, computational efficiency, simplicity and therefore relatively few tuning parameters, the classical linear mixed model and regularized regression methods are likely to remain strong contenders for genomic prediction. Conclusions: The dependence of predictive performance and computational burden on target datasets and traits call for increasing investments in enhancing the computational efficiency of machine learning algorithms and computing resources.
Correction to: Assessing the efficiency and heritability of blocked tree breeding trials
(2024) Piepho, Hans-Peter; Williams, Emlyn; Prus, Maryna
Assessing the efficiency and heritability of blocked tree breeding trials
(2024) Piepho, Hans-Peter; Williams, Emlyn; Prus, Maryna
Progeny trials in tree breeding are often laid out using blocked experimental designs, in which families are randomly assigned to plots and several trees are planted per plot. Such designs are optimized for the assessment of family effects. However, tree breeders are primarily interested in assessing breeding values of individual trees. This paper considers the assessment of heritability at both the family and tree levels. We assess heritability based on pairwise comparisons among individual trees. The approach shows that there is considerable heterogeneity in pairwise heritabilities, primarily due to the differences in both genetic as well as error variances among within- and between-family comparisons. Our results further show that efficient blocking positively affects all types of comparison except those among trees within the same plot.
To move or not to move—factors influencing small-scale herder and livestock movements in the Dzungarian Gobi, Mongolia
(2023) Michler, Lena M.; Kaczensky, Petra; Oyunsaikhan, Ganbaatar; Bartzke, Gundula S.; Devineau, Olivier; Treydte, Anna C.
In Mongolia, where nomadic pastoralism is still practiced by around one-third of the population, increasing livestock numbers, socio-economic constraints and climate change raise concerns over rangeland health. Little empirical evidence explains what triggers camp moves of pastoralists in the Dzungarian Gobi in Mongolia, which factors influence grazing mobility around camps, and how altitudinal migration benefits small livestock. We combined GPS tracking data of 19 small livestock herds monitored from September 2018 to April 2020 with remotely sensed climate and environmental data. We used general linear-mixed models to analyse variables influencing camp use duration and daily mobility patterns. To understand the importance of the altitudinal migration, we compared climatic conditions along the elevation gradient and looked at seasonal body weight changes of small livestock. We found that available plant biomass and season best explained camp use duration. Daily walking distance and maximum distance from camp increased with camp use duration. Pasture time increased with increasing biomass and rising temperatures. We conclude that herders in the Dzungarian Gobi have optimized pasture use by reacting to changes in biomass availability at landscape and local scale, and by embracing altitudinal migration. Flexibility in grazing mobility seems to have enabled local herder communities to practise sustainable pasture use. Maintaining this mobility will most likely be the best strategy to deal with environmental change under the current climate change scenarios.
Effect of phosphorus fertilizer placement depth, amount, and soil water content on early maize growth
(2025) Ning, Fangfang; Nkebiwe, Peteh Mehdi; Munz, Sebastian; Hartung, Jens; Zhang, Ping; Huang, Shoubing; Graeff‐Hönninger, Simone; Ning, Fangfang; Department of Agronomy (340a), Institute of Crop Science, University of Hohenheim, Stuttgart, Germany; Nkebiwe, Peteh Mehdi; Department of Fertilization and Soil Matter Dynamics (340i), Institute of Crop Science, University of Hohenheim, Stuttgart, Germany; Munz, Sebastian; Department of Agronomy (340a), Institute of Crop Science, University of Hohenheim, Stuttgart, Germany; Hartung, Jens; Biostatistics Unit (340c), Institute of Crop Science, University of Hohenheim, Stuttgart, Germany; Zhang, Ping; Institute of Maize and Featured Upland Crops, Zhejiang Academy of Agricultural Sciences, Dongyang, China; Huang, Shoubing; College of Agronomy and Biotechnology, China Agricultural University, Beijing, China; Graeff‐Hönninger, Simone; Department of Agronomy (340a), Institute of Crop Science, University of Hohenheim, Stuttgart, Germany
Background: Drought stress (DS) reduces soil phosphorus (P) availability by limiting P diffusion and uptake, while global P resource scarcity exacerbates nutrient limitations for crops. Aim: This study investigated whether deep subsurface P placement could alleviate the combined effects of P deficiency and DS on maize growth. Methods: A greenhouse trial with maize (cv. Ricardinio) was conducted involving three factors: three P fertilizer amounts (0 mg P pot −1 [NP], 109 mg P pot −1 [LP], and 655 mg P pot −1 [HP]), three placement depths (0–9 cm [U, upper layer], 9–18 cm [L, lower layer], and uniformly mixed throughout 0–18 cm [M]), and two soil water contents (45% of soil water holding capacity [WHC] [DS] and 75% WHC [WW]). Root and shoot traits were assessed at the fourth‐ and tenth‐leaf stages. Results: LP significantly reduced shoot biomass and P content compared to HP treatment. At the fourth‐leaf stage, DS increased root biomass by 69.3% and 27.1% in the 9–18 cm and 0–18 cm layers compared to WW treatment. At the tenth‐leaf stage, DS reduced root biomass by at least 41% across layers and decreased shoot growth and P uptake. Under DS, L‐DS increased root growth and root length in the 9–18 cm layer compared to M‐DS and U‐DS treatments but did not improve shoot traits. Conclusion: Deep subsurface P placement promoted deeper root development under drought and P deficiency. However, its benefits on shoot growth were not evident in early stages, indicating the need for longer term field validation.
Optimizing mung bean productivity and root morphology with biofertilizers for sustainable farming
(2025) Nabati, Jaafar; Mirzaeetalarposhti, Reza; Yousefi, Afsaneh; Kurdestani, Ali Malakshahi; Nabati, Jaafar; Department of Agrotechnology, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran; Mirzaeetalarposhti, Reza; Institute of Crop Science, University of Hohenheim, 70599, Stuttgart, Germany
The excessive use of chemical fertilizers has raised major environmental and economic concerns in legume cultivation. This study assessed the effects of various fertilizers, including biofertilizers and chemical nitrogen, on yield, root characteristics, and nutrient dynamics in two mung bean genotypes (Partow, IC418452). Field trials were conducted over a two-year period in Mashhad, Iran, using a factorial randomized block design. Treatments included two genotypes and six fertilizer levels: control, N-fixing bacteria (FLNF), P-solubilizing bacteria (PSB), K-solubilizing bacteria (KSB), a consortium (FLNF + PSB + KSB), and Urea. Yield components, biomass, root morphology, nodulation, and plant/soil NPK concentrations were measured and analyzed. Fertilizers significantly affected yield, biomass, root structure, and nutrient uptake. Urea yielded the highest biomass, grain yield, and root area, especially in Partow. However, the microbial consortium significantly improved yield components compared to the control and uniquely maximized root nodulation and inoculation percentage, indicating an enhanced biological nitrogen fixation potential. On average across the two seasons, urea increased grain yield by 46% and biomass by 41% relative to the control, whereas the microbial consortium enhanced root nodulation by 62% and yield by 32%. Significant genotype×fertilizer interactions highlighted genotype-specific responses. Nodulation correlated positively with yield. Both urea and the PGPR consortium significantly increased mung bean productivity. While urea maximized yield, the consortium provided considerable yield gains and enhanced biological nitrogen fixation potential, presenting a viable and sustainable alternative to reduce reliance on chemical nitrogen. The goal was to understand the relationships between root morphology, nutrient utilization, and yield to promote sustainable, high-yield mung bean cultivation. These findings highlight the potential of multi-strain biofertilizers to maintain mung-bean productivity while reducing dependence on synthetic N inputs.
Miscanthus‐derived products for material applications: can they contribute to greenhouse gas emission mitigation?
(2025) Lask, Jan; Weik, Jan; Kiesel, Andreas; Lewandowski, Iris; Wagner, Moritz; Lask, Jan; Institute of Crop Science, University of Hohenheim, Stuttgart, Germany; Weik, Jan; Institute of Crop Science, University of Hohenheim, Stuttgart, Germany; Kiesel, Andreas; Institute of Crop Science, University of Hohenheim, Stuttgart, Germany; Lewandowski, Iris; Institute of Crop Science, University of Hohenheim, Stuttgart, Germany; Wagner, Moritz; Institute of Applied Ecology, Geisenheim University, Geisenheim, Germany
Miscanthus is a particularly promising lignocellulosic biomass as it can also grow under marginal conditions and can be used for a wide range of products including energy and material applications. The latter, including applications in the construction, textile, chemical, or agricultural sector, is becoming increasingly relevant today. In general, it is hypothesised that biobased products are advantageous in terms of their greenhouse gas (GHG) performance when compared to conventional—in particular fossil—alternatives. To investigate this, the life cycle assessment methodology is typically applied. However, assessments are subject to uncertainty and variability due to assumptions and methodological choices. Given the increasing interest in miscanthus‐derived material applications, this study aims to draw more general conclusions about their GHG performance and relative mitigation potential. This should support a better understanding of their contribution to climate change mitigation objectives and guide the selection of promising products or product groups. A systematic review of peer‐reviewed literature was conducted. In total, 20 studies reporting on 188 comparisons of the GHG performance of miscanthus‐derived and alternative products were assessed. Most comparisons indicated potential GHG mitigation through miscanthus‐derived products, with the majority ranging between 20% and 100% savings. Key parameters defining the relative performance include the selection of the reference product, consideration of soil carbon changes, changes in product and process design, as well as the incorporation of indirect Land Use Change (iLUC) impacts. Overall, we conclude that miscanthus‐derived material applications have the potential to contribute to GHG emission mitigation if iLUC effects are minimised. Given the limited availability of agricultural land, miscanthus‐derived products with high absolute GHG mitigation potential per unit of biomass used and long product lifetime are preferable. For future development, potential environmental trade‐offs need to be monitored.
Viroid ecology in hops (Humulus lupulus L.): high prevalence in commercial systems but low presence in wild populations
(2026) Jagani, Swati; Krönauer, Christina; Born, Ute; Hagemann, Michael Helmut; Jagani, Swati; Department of Production Systems of Horticultural Crops, University of Hohenheim, Stuttgart, Germany; Krönauer, Christina; Bavarian State Research Center for Agriculture, Institute for Crop Science and Plant Breeding, Wolnzach, Germany; Born, Ute; Department of Production Systems of Horticultural Crops, University of Hohenheim, Stuttgart, Germany; Hagemann, Michael Helmut; Department of Production Systems of Horticultural Crops, University of Hohenheim, Stuttgart, Germany
Introduction: Hop (Humulus lupulus L.), a vital crop in the brewing industry, is increasingly threatened by infections caused by viroids and viruses. The extensive use of vegetative propagation in hop cultivation facilitates the accumulation and dissemination of these pathogens. However, little is known about their prevalence and ecological behavior in non-commercial settings. This study provides a comprehensive overview of viroid and virus infections across Germany, with particular attention to their occurrence and potential transmission across commercial, settlement, and wild hop populations. Methods: Between 2020 and 2023, 418 hop leaf samples from commercial (n = 345), settlement (n = 29), and wild (n = 44) populations were collected. Viroid and virus detection was performed using RT-PCR and PCR. To investigate possible cross-species transmission and sequence variation, HSVd-positive samples from hops and nearby grapevines were further analyzed via Sanger sequencing. Results: Viroid screening revealed that the citrus bark cracking viroid (CBCVd; Cocadviroid rimocitri) was confined to commercial hop cultivation. This study also marks the first confirmed detection of hop stunt viroid (HSVd; Hostuviroid impedihumuli) in commercial hop fields in Germany. Virus screening showed that hop latent virus (HpLV; Carlavirus latenshumuli) and american hop latent virus (AHpLV; Carlavirus americanense) were exclusively found in commercial hops. Hop mosaic virus (HpMV; Carlavirus humuli) was detected across all three groups—commercial, settlement, and wild populations. Arabis mosaic virus (ArMV; Nepovirus arabis) and apple mosaic virus (ApMV; Ilarvirus ApMV) were identified in both commercial and wild hops but were absent from settlement samples. Overall, commercial hop populations exhibited the highest pathogen burden, frequently harboring multiple viroid and virus infections. These findings underscore the importance of using certified, pathogen-free planting material, implementing early detection strategies, and updating plant passport regulations to include high-risk pathogens. While prevalence estimates reflect risk-based sampling from key production regions, the study provides a solid basis for enhancing pathogen surveillance and improving preventive measures in hop cultivation.
Operational principles for fostering transformative qualities and capacities in higher education sustainability science and practice
(2026) Fagerholm, Nora; Coles, Neil; Beery, Thomas; Torralba, Mario; Hakkarainen, Viola; Albert, Christian; Andersson, Erik; Bergström, Ryan; Bieling, Claudia; Gentin, Sandra; Klonner, Carolin; Olafsson, Anton Stahl; Raymond, Christopher; Rouhiainen, Henna; Wamsler, Christine; Fagerholm, Nora; Department of Geography and Geology, University of Turku, 20114, Turku, Finland; Coles, Neil; Institute of Agriculture, The University of Western Australia, Crawley, 6009, Perth, WA, Australia; Beery, Thomas; Sustainable Multifunctional Landscapes Research Group, Kristianstad University, Kristianstad, Sweden; Torralba, Mario; Environmental Geography Group, IVM Institute for Environmental Studies, VU University Amsterdam, Amsterdam, The Netherlands; Hakkarainen, Viola; Faculty of Sustainability, Leuphana University Lüneburg, Lüneburg, Germany; Albert, Christian; Institute of Environmental Planning, Leibniz University Hannover, Herrenhaeuser Str. 2, 30419, Hannover, Germany; Andersson, Erik; Ecosystems and Environment Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, 00014, Helsinki, Finland; Bergström, Ryan; Sustainable Multifunctional Landscapes Research Group, Kristianstad University, Kristianstad, Sweden; Bieling, Claudia; Division Societal Transition and Agriculture (430B), Institute of Social Sciences in Agriculture, University of Hohenheim, Stuttgart, Germany; Gentin, Sandra; Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark; Klonner, Carolin; Department of Geography and Geology, University of Turku, 20114, Turku, Finland; Stahl Olafsson, Anton; Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark; Raymond, Christopher; Ecosystems and Environment Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, 00014, Helsinki, Finland; Rouhiainen, Henna; Biodiversity Unit, University of Turku, Turku, Finland; Wamsler, Christine; Lund University Centre for Sustainability Studies (LUCSUS), Lund, Sweden
Education for sustainability is widely recognised as a critical pathway for driving the transformations needed to address today’s polycrisis. Despite growing theoretical and conceptual advancements in sustainability education, current approaches have not achieved the deep systemic changes required. While university networks and individual institutions increasingly integrate sustainability into their education, concerns persist that transformative learning agendas often remain superficial. In particular, they frequently fail to equip learners with the emotional resilience and skills necessary to engage effectively with complex global challenges, as well as educators with the pedagogical framework to facilitate such learning. This article emphasises the need to advance transformative learning in sustainability science and practice in higher education by addressing the inner dimensions of sustainability: our individual and collective values, beliefs, worldviews, and associated transformative qualities and capacities. This means targeting deep leverage points and meaningful change by supporting more relational approaches, including an integrated inner–outer change in being, thinking, and acting. We provide seven operational principles for supporting the inner–outer transformation towards sustainability in learning and teaching sustainability science and practice, especially in geography and related fields. These principles highlight the importance of nurturing five clusters of transformative qualities and capacities—awareness, connection, insight, purpose, and agency—guided by relational approaches required to support profound and integrative learning experiences. We provide concrete examples of how to implement these principles. The proposed principles aim to inspire educators and learners to deeply engage with sustainability challenges to contribute to transformative change across individual, collective, and system levels.
Impact of different growing substrates on growth, yield and cannabinoid content of two Cannabis sativa L. genotypes in a pot culture
(2020) Burgel, Lisa; Hartung, Jens; Graeff-Hönninger, Simone
The impacts of different growing substrate compositions, consisting of peat (PM), peat substituted with 30% green fibre (G30) and coco coir fibre (CC) growth media, were investigated in regard to the plant height, biomass and floral yield, biomass nitrogen (N) content, root growth, and cannabidiol content (CBD/A) of two phytocannabinoid-rich cannabis genotypes in an indoor pot cultivation system. Genotypes and substrate treatment combinations were randomly allocated to 36 plants according to a Latin square design. The results showed a higher total plant height for PM (39.96 cm), followed by G30 (35.28 cm), and the lowest in CC (31.54 cm). The N content of leaves indicated the highest values for plants grown in G30 (52.24 g kg DW−1), followed by PM (46.75 g kg DW−1) and a significantly lower content for CC (37.00 g kg DW−1). Root length density (RLD) increased by 40% (PM) and 50% (G30), compared to CC treatments, with no significant differences in root dry weight. Both genotypes, Kanada (KAN) and 0.2x, reacted in a genotype-specific manner. KAN indicated a reduced floral yield of plants grown in G30 (4.94 g plant−1) and CC (3.84 g plant−1) compared to PM (8.56 g plant−1). 0.2x indicated stable high floral yields of 9.19 g plant−1 (G30) to 7.90 g plant−1 (CC). Leaf DW increased in PM (5.78 g plant−1) and G30 (5.66 g plant−1) compared to CC (3.30 g plant−1), while CBD/A content remained constant. Due to a higher biomass yield, the CBD/A yield of flowers (549.66 mg plant−1) and leaves (224.16 mg plant−1) revealed 0.2x as an interesting genotype for indoor pot cultivation in a peat-based substrate substituted with 30% green fibres. Overall, the demand for organic green fibres to partly replace fractionated peat showed a genotype-specific option for a homogeneous plant development, with comparable high biomass yields and stable cannabinoid contents compared to a peat containing standard substrate.
Towards more nuanced narratives in bioeconomy strategies and policy documents to support knowledge-driven sustainability transitions
(2025) Stoye, Juliane; Schlaile, Michael P.; von Cossel, Moritz; Bertacchi, Stefano; Escórcio, Rita; Winkler, Bastian; Curran, Thomas P.; Ní Chléirigh, Laoise; Nic an Bhaird, Máire; Klakla, Jan Bazyli; Nachtergaele, Pieter; Ciantar, Hailey; Scheurich, Philipp; Lewandowski, Iris; Reinmuth, Evelyn; Hopmans, Jan
The bioeconomy has been discussed as a key strategy for addressing sustainability challenges, particularly regarding the transition from fossil-based to bio-based systems, in numerous national and supranational strategies and policy documents related to the bioeconomy. However, public understanding of and engagement with the bioeconomy remains limited. This is partly due to the bias of many bioeconomy strategies and policy documents towards technological solutions that tend to overlook the social, normative, and transformative dimensions of systemic change as well as the necessary knowledge. This opinion paper explores the potential of narratives as a means of communicating bioeconomy research in public policy, with the aim of addressing the communication gap between science, policy, and society. When applied in responsible and nuanced ways that acknowledge their embeddedness and context, bioeconomy (policy) narratives can support sensemaking for science communication, improve public understanding, facilitate stakeholder engagement and behavioural change. We argue that such narrative approaches can help to create narrative ‘boundary objects’ that can support more inclusive and participatory processes, enabling the co-creation of transformative knowledge for bioeconomy transitions with stakeholders as active participants. In summary, we highlight several opportunities, as well as limitations and implications, that could inform future work on bioeconomy narratives.
Deviation from the regression of yield on nitrogen fertiliser rate as a tool for detecting fraud in organic banana production
(2025) Benzing, Albrecht; Piepho, Hans‐Peter; Orr, Ryan; Ullauri, Juan‐Carlos
Background and aims: Bananas are demanding in nitrogen (N) input; therefore, there is a temptation for organic farmers for using synthetic N fertilisers, which are not allowed under organic standards. The aim of our study was to develop a tool that identifies high banana yields obtained with suspiciously low organic N input. Methods: We systematically reviewed literature from experimental studies on N fertilisation in bananas from all over the world. We also developed a simplified N balance model for organic bananas. Furthermore, N fertilisation and banana yield data from organic and conventional farmers in different countries were collected. From these, a subset of trustworthy organic farms was identified, as a reference concerning plausible ratios of yield versus fertilisation. A model was developed to estimate the deviation from the regression of trustworthy farms and thus identify suspicious cases. Results: Neither literature nor the N balance led to a meaningful benchmark for differentiating plausible from non‐plausible yields. The regression of yield on N fertiliser rate from the trustworthy organic farmers, however, turned out to be a helpful reference, and the deviation from this regression helps to achieve our aim. Depending on the alert limit, that is, the probability of obtaining false positive results, 4, 6, or 9 out of 157 data‐pairs from organic farmers turned out to be suspicious. Conclusion: Measuring deviation from the regression of the trustworthy farms is a useful tool for identifying organic banana farmers suspected to be using synthetic N fertilisers but is not in itself a proof of fraud. The model will improve as more data becomes available.
How equal space seeding in maize (Zea mays L.) influences weed competition, crop growth, and grain yield
(2025) Naruhn, Georg‐Peter; Hartung, Jens; Schulz, Vanessa; Möller, Kurt; Gerhards, Roland
The increase in herbicide‐resistant weeds and new political guidelines force farmers to change their weed management strategies while reducing herbicides. Current study aimed to explore the potential of equal space seeding (ESS) in maize ( Zea mays L.) compared to conventional row seeding (CRS) regarding weed suppression and crop development. A multisite experiment was carried out in 2022 in southwestern Germany comparing ESS and CRS by pooling the data of three treatments (untreated, herbicide, and hoeing) of each system. The parameters leaf area index (LAI), photosynthetic active radiation (PAR), maize and weed biomass, and grain yield were measured. The ESS was neither statistically different in terms of LAI and PAR absorption nor did it show a higher weed suppression due to a reduced weed biomass compared to CRS. Although two of three trials also showed no differences between both systems in grain yield, the experiment with the lowest rainfall and irrigation amount as well as the evaluation across all three sites showed a significant increase in yield from ESS (5.72 Mg ha −1 ) compared to CRS (3.77 Mg ha −1 ). It was assumed that a reduced intraspecific competition, a slightly higher PAR absorption during maize flowering, and an improved root growth contributed to the higher yields in the ESS system. For more evidence as well as for a reliable recommendation for a specific cropping system, further studies in different environments are needed.
Bird species richness and diversity responses to land use change in the Lake Victoria Basin, Kenya
(2024) Mugatha, Simon M.; Ogutu, Joseph O.; Piepho, Hans-Peter; Maitima, Joseph M.
The increasing demand for cultivated lands driven by human population growth, escalating consumption and activities, combined with the vast area of uncultivated land, highlight the pressing need to better understand the biodiversity conservation implications of land use change in Sub-Saharan Africa. Land use change alters natural wildlife habitats with fundamental consequences for biodiversity. Consequently, species richness and diversity typically decline as land use changes from natural to disturbed. We assess how richness and diversity of avian species, grouped into feeding guilds, responded to land use changes, primarily expansion of settlements and cultivation at three sites in the Lake Victoria Basin in western Kenya, following tsetse control interventions. Each site consisted of a matched pair of spatially adjacent natural/semi-natural and settled/cultivated landscapes. Significant changes occurred in bird species richness and diversity in the disturbed relative to the natural landscape. Disturbed areas had fewer guilds and all guilds in disturbed areas also occurred in natural areas. Guilds had significantly more species in natural than in disturbed areas. The insectivore/granivore and insectivore/wax feeder guilds occurred only in natural areas. Whilst species diversity was far lower, a few species of estrildid finches were more common in the disturbed landscapes and were often observed on the scrubby edges of modified habitats. In contrast, the natural and less disturbed wooded areas had relatively fewer estrildid species and were completely devoid of several other species. In aggregate, land use changes significantly reduced bird species richness and diversity on the disturbed landscapes regardless of their breeding range size or foraging style (migratory or non-migratory) and posed greater risks to non-migratory species. Accordingly, land use planning should integrate conservation principles that preserve salient habitat qualities required by different bird species, such as adequate patch size and habitat connectivity, conserve viable bird populations and restore degraded habitats to alleviate adverse impacts of land use change on avian species richness and diversity.
Hierarchical modelling of variance components makes analysis of resolvable incomplete block designs more efficient
(2024) Studnicki, Marcin; Piepho, Hans Peter
The standard approach to variance component estimation in linear mixed models for alpha designs is the residual maximum likelihood (REML) method. One drawback of the REML method in the context of incomplete block designs is that the block variance may be estimated as zero, which can compromise the recovery of inter-block information and hence reduce the accuracy of treatment effects estimation. Due to the development of statistical and computational methods, there is an increasing interest in adopting hierarchical approaches to analysis. In order to increase the precision of the analysis of individual trials laid out as alpha designs, we here make a proposal to create an objectively informed prior distribution for variance components for replicates, blocks and plots, based on the results of previous (historical) trials. We propose different modelling approaches for the prior distributions and evaluate the effectiveness of the hierarchical approach compared to the REML method, which is classically used for analysing individual trials in two-stage approaches for multi-environment trials.
Mapping knowledge domains of regenerative agriculture with a focus on on-farm nitrogen fertilization experimentation and response surface regression
(2025) Abdipourchenarestansofla, Morteza; Piepho, Hans-Peter
In the face of growing environmental concerns and the global demand for sustainable agriculture, achieving balanced nitrogen (N) management is critical for both maximizing crop productivity and maintaining environmental health. This dissertation proposes an innovative framework to address this challenge within the scope of regenerative agriculture, which emphasizes sustainable farming practices. Regenerative agriculture aims to reduce chemical inputs while maintaining yield levels yet implementing these practices at scale is complex due to the intricate interactions between biological, environmental, and technological factors on farms. This research tackles these challenges by introducing a Knowledge Domain Mapping (KDM)-based framework, integrating advanced technologies—including remote sensing, Internet of Things (IoT) telemetry, geospatial sciences, statistical modeling, machine learning, and cloud computing—to create a holistic and scalable system for optimizing nitrogen applications. Central to this research is the accurate estimation and spatial allocation of the Economic Optimum Nitrogen Rate (EONR), a crucial element for reducing nitrogen use and enhancing yield. A key contribution of this study is the development of a robust Response Surface Model (RSM) that leverages multispectral indices (MSIs) from Sentinel-2 imagery, historical IoT telemetry data, and on-machine nitrogen sensors. This RSM approach allows for precise EONR predictions tailored to field-specific conditions, reducing the need for traditional plot-based trials and achieving an average prediction error of just 14.5%. Applied to a 7-hectare winter wheat field, the model successfully predicted EONR values ranging from 43 kg/ha to 75 kg/ha across zones, showcasing the adaptability and accuracy of RSM for field-specific nitrogen recommendations. This precisionfocused approach exemplifies the study’s goal of minimizing environmental impacts while ensuring sustainable yield improvements. Beyond the initial field-level implementation, this research examines the generalizability of the RSM framework using two modeling strategies: a single RSM across fields and a weighted average model that aggregates individual field-specific RSMs. The weighted model demonstrated superior adaptability and high prediction accuracy, with a root mean square error (RMSE) of 11 kg N/ha for the EONR, highlighting the framework’s potential for broader application across different agricultural settings. Such generalizability supports the framework’s adoption in diverse farming environments, enabling precise and informed nitrogen management at scale. To facilitate widespread adoption and practical application, the dissertation also introduces a cloud-based infrastructure that integrates the KDM framework with real-time IoT data and satellite imagery. Leveraging cloud services like Amazon Web Services (AWS) Batch for job orchestration, Amazon S3 for scalable data storage, and RDS Postgres for structured data management, this8 infrastructure allows for seamless handling of both real-time and historical data. Spatial interpolation techniques, such as Kriging, enhance the model’s capability to generate real-time nitrogen prescription maps, enabling precise nutrient management for large-scale agricultural operations. Automated data quality control and data harmonization embedded within this cloud architecture provide a strong foundation for managing increasing data volumes and diverse field conditions, making the system cost-effective, adaptable, and efficient for modern agriculture. In summary, this dissertation maps regenerative agriculture via a comprehensive roadmap for translating regenerative agriculture principles into practical, operational nitrogen management practices. Through KDM an interdisciplinary approach is mapped by the integration of advanced modeling, data processing, and cloud technologies. This framework enables sustainable crop management and aligns with global goals for environmentally responsible food production. The innovations introduced here support a scalable, data-driven approach to agricultural sustainability, bridging scientific research with real-world applications to meet the evolving demands of modern agriculture.
Metabolic profiling of ‘Elstar’ and ‘Nicoter’ apples: impact of storage time, dynamic controlled atmosphere and 1-MCP treatment
(2024) Thewes, Fabio Rodrigo; Büchele, Felix; Uhlmann, Lilian Osmari; Lugaresi, Adriana; de Oliveria Neuwald, Daiane Quadros; Brackmann, Auri; Both, Vanderlei; Wagner, Roger; Neuwald, Daniel Alexandre; Yao, Jia-Long
The aim of this work was to evaluate the effect of CA and DCA on sugars, tricarboxylic acid cycle (TCA), anaerobic metabolism and some volatile compounds of ‘Elstar’ and ‘Nicoter’ apples. This study also aimed to evaluate the effect of ethylene action blocking by 1-MCP (0.650 ppm). The storage conditions tested for both cultivars were (1) CA; (2) DCA-CF; (3) DCA-RQ 1.3; (4) DCA-RQ 1.5; (5) DCA-CD 1.1; and (6) DCA-CD 1.3. The lowest oxygen limit (LOL) was higher for the ‘Nicoter’ apples, and the three DCA methods were able to detect this difference between the cultivars. Sorbitol had a trend of accumulation when the fruit was stored under DCA-RQ and DCA-CD, especially in higher RQ and CD, showing a negative Pearson correlation with the oxygen partial pressure over the storage period. The 1-MCP treatment induced sorbitol accumulation even when the fruit was stored under CA. The TCA intermediaries, such as citrate, 2-oxoglutarate, succinate, fumarate and oxaloacetate, were the most affected by the atmosphere conditions and the 1-MCP treatment for both cultivars. Malic acid was more affected by the storage time than the atmosphere conditions. Succinate and fumarate had an accumulation trend when the fruit was stored under DCA-RQ.

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