Institut für Kulturpflanzenwissenschaften

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Bayesian‐optimized experimental designs for estimating the economic optimum nitrogen rate: a model‐averaging approach
(2025) Matavel, Custódio Efraim; Meyer‐Aurich, Andreas; Piepho, Hans‐Peter
Field experiments play a crucial role in optimizing nutrient application strategies and determining the economic optimum nitrogen rate (EONR), aiding stakeholders in agricultural decision‐making. These experiments tailor agricultural input management to maximize efficiency and sustainability, ultimately improving farm economics. However, the optimal setup of field experiments remains an ongoing debate, particularly regarding economic considerations such as the selection of treatment levels (design points), their spatial arrangement, and the number of replications required for statistical validity and cost‐effectiveness. This study optimizes field experiments for estimating the EONR using a model‐averaging approach within a Bayesian framework. We employed Bayesian inference and the No‐U‐turn sampler to integrate model averaging across multiple yield response models, improving robustness in EONR estimation. Stochastic optimization, specifically simultaneous perturbation stochastic approximation, was used to optimize experimental designs, and their performance was evaluated through Monte Carlo simulations. Our results show that optimized experimental designs significantly improve the precision of EONR estimates. Designs incorporating higher number of nitrogen levels provided the best trade‐off between accuracy and efficiency, minimizing bias and mean squared error. Even with a fixed total number of plots (120), increasing the number of design points resulted in lower variance, demonstrating the efficiency of well‐structured experimental designs. This research lays the groundwork for future developments in experimental methodologies with wide‐ranging implications for agricultural economics and policymaking, ultimately supporting better‐informed decision‐making. Future work should integrate environmental constraints and account for real‐world variability in treatment replication to further refine experimental optimization strategies.
Breeding progress of disease resistance and impact of disease severity under natural infections in winter wheat variety trials
(2021) Laidig, F.; Feike, T.; Hadasch, S.; Rentel, D.; Klocke, B.; Miedaner, T.; Piepho, H. P.
Key message: Breeding progress of resistance to fungal wheat diseases and impact of disease severity on yield reduction in long-term variety trials under natural infection were estimated by mixed linear regression models. Abstract: This study aimed at quantifying breeding progress achieved in resistance breeding towards varieties with higher yield and lower susceptibility for 6 major diseases, as well as estimating decreasing yields and increasing disease susceptibility of varieties due to ageing effects during the period 1983–2019. A further aim was the prediction of disease-related yield reductions during 2005–2019 by mixed linear regression models using disease severity scores as covariates. For yield and all diseases, overall progress of the fully treated intensity (I2) was considerably higher than for the intensity without fungicides and growth regulators (I1). The disease severity level was considerably reduced during the study period for mildew (MLD), tan spot (DTR) and Septoria nodorum blotch (ear) (SNB) and to a lesser extent for brown (leaf) rust (BNR) and Septoria tritici blotch (STB), however, not for yellow/stripe rust (YLR). Ageing effects increased susceptibility of varieties strongly for BNR and MLD, but were comparatively weak for SNB and DTR. Considerable yield reductions under high disease severity were predicted for STB (−6.6%), BNR (−6.5%) and yellow rust (YLR, −5.8%), but lower reductions for the other diseases. The reduction for resistant vs. highly susceptible varieties under high severity conditions was about halved for BNR and YLR, providing evidence of resistance breeding progress. The empirical evidence on the functional relations between disease severity, variety susceptibility and yield reductions based on a large-scale multiple-disease field trial data set in German winter wheat is an important contribution to the ongoing discussion on fungicide use and its environmental impact.
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
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.
Combined bioenergy and food potential of Opuntia ficus-indica grown on marginal land in rural Mexico
(2024) Varela Pérez, Paola; Winkler, Bastian; Röcker, Philip; von Cossel, Moritz; Rubiera González, Fernando
Opuntia ficus-indica (cactus pear) emerged as a promising crop for sustainable bioenergy production on marginal agricultural land, mitigating competition with food crops and lowering the risk of other indirect land use changes. In this study, the bioenergy potential is investigated of cactus pear residues within a smallholder farming context of Nopaltepec, a rural municipality in Central Mexico. Nopaltepec is a native environment of cactus pear and shows an annual production volume of 30 Gg of fresh matter. A bottom-up approach employing semi-structured interviews ( n = 16) was utilized to assess the feasibility of transforming the pruning residues of cactus pear into a viable bioenergy source. The results indicate a substantial bioenergy potential, with 27 Mg of fresh matter biomass (equivalent to 9720 m 3 biogas) per hectare obtainable annually without compromising fruit yields. Moreover, the digestate produced through anaerobic digestion can be recycled as biofertilizer, offering economic and ecological advantages to smallholders. Notably, farmers expressed keen interest in integrating this technology into their agricultural systems. This research underscores the potential of cactus pear residues for developing a decentralized bioenergy sector and provides valuable ideas for future bottom-up assessments in rural communities like Nopaltepec.
Comparative effects of individual and consortia plant growth promoting bacteria on physiological and enzymatic mechanisms to confer drought tolerance in maize (Zea mays L.)
(2021) Saleem, Muhammad; Nawaz, Fahim; Hussain, Muhammad Baqir; Ikram, Rao Muhammad
Mitigation strategies based on plant–microbe interactions to increase the performance of plants under water-deficit conditions are well documented. However, little is known about a suitable consortium of bacterial inoculants and underlying physiological and enzymatic events to improve drought tolerance in maize. We performed laboratory and pot experiments to understand the synergistic interactions among plant growth-promoting bacteria to alleviate the drought-induced damages in maize. Initially, ten bacterial strains were evaluated for their osmotic stress tolerance capacity by growing them in a media containing 0, 10, 20, and 30% polyethylene glycol (PEG-6000). Also, the seeds of a drought tolerant (NK-6654) and sensitive (SD-626) maize cultivar were inoculated with these bacterial strains in the first pot experiment to determine their effects on the growth and physiological processes. Later, in the second pot experiment, the best performing inoculants were selected to study the individual and synergistic effects of bacterial inoculation to confer drought tolerance in maize. Our findings showed that the inoculation with tolerant strains resulted in higher photosynthetic activity (25–39%), maintenance of leaf water status (14–18%) and pigments (27–32%), and stimulation of antioxidant machinery (28–38%) than no inoculation in water-stressed maize seedlings. Moreover, the treatment with bacteria consortia further stimulated the drought protective mechanisms and resulted in higher efficiency of photosynthetic (47–61%) and antioxidant systems (42–62%) than the individual inoculants under water-deficit conditions. We conclude that the inoculation with microbial consortia regulates water uptake, photosynthetic performance, and stress metabolites to minimize drought-induced damages in maize.
Computational aspects of experimental designs in multiple-group mixed models
(2023) Prus, Maryna; Filová, Lenka
We extend the equivariance and invariance conditions for construction of optimal designs to multiple-group mixed models and, hence, derive the support of optimal designs for first- and second-order models on a symmetric square. Moreover, we provide a tool for computation of D - and L -efficient exact designs in multiple-group mixed models by adapting the algorithm of Harman et al. (Appl Stoch Models Bus Ind, 32:3–17, 2016). We show that this algorithm can be used both for size-constrained problems and also in settings that require multiple resource constraints on the design, such as cost constraints or marginal constraints.
Computing optimal allocation of trials to subregions in crop‐variety testing in case of correlated genotype effects
(2025) Prus, Maryna
The subject of this work is the allocation of trials to subregions in crop variety testing in the case of correlated genotype effects. A solution for computation of optimal allocations using the OptimalDesign package in R is proposed. The obtained optimal designs minimize linear criteria based on the mean squared error matrix of the best linear unbiased prediction of the genotype effects. The proposed computational approach allows for any kind of additional linear constraint on the designs. The results are illustrated by a real data example.
Degradation of hop latent viroid during anaerobic digestion of infected hop harvest residues
(2021) Hagemann, Michael Helmut; Born, Ute; Sprich, Elke; Seigner, Luitgardis; Oechsner, Hans; Hülsemann, Benedikt; Steinbrenner, Jörg; Winterhagen, Patrick; Lehmair, Erich
The citrus bark cracking viroid (CBCVd) was identified as causal agent for a severe stunting disease in hops. Viroids are highly stable parasitic RNAs, which can be easily transmitted by agricultural practices. Since CBCVd has recently been detected in two European countries a growing concern is that this pathogen will further spread and thereby threaten the European hop production. Biogas fermentation is used to sanitize hop harvest residues infected with pathogenic fungi. Consequently, the aim of this study was to test if biogas fermentation can contribute to viroid degradation at mesophilic (40 °C) and thermophilic (50 °C) conditions. Therefore, a duplex reverse transcription real-time PCR analysis was developed for CBCVd and HLVd detection in biogas fermentation residues. The non-pathogenic hop latent viroid (HLVd) was used as viroid model for the pathogenic CBCVd. The fermentation trials showed that HLVd was significantly degraded after 30 days at mesophilic or after 5 days at thermophilic conditions, respectively. However, sequencing revealed that HLVd was not fully degraded even after 90 days. The incubation of hop harvest residues at different temperatures between 20 and 70 °C showed that 70 °C led to a significant HLVd degradation after 1 day. In conclusion, we suggest combining 70 °C pretreatment and thermophilic fermentation for efficient viroid decontamination.
Description and prediction of copper contents in soils using different modeling approaches - results of long‐term monitoring of soils of northern Germany
(2022) Ludwig, Bernard; Klüver, Karen; Filipinski, Marek; Greenberg, Isabel; Piepho, Hans‐Peter; Cordsen, Eckhard
Background: Different regression approaches may be useful to predict dynamics of copper (Cu), an essential element for plants and microorganisms that becomes toxic at increased contents, in soils. Aim: Our objective was to explore the usefulness of mixed-effects modeling and rule-based models for a description and prediction of Cu contents in aqua regia (CuAR) in surface soils using site, pH, soil organic carbon (SOC), and the cation exchange capacity (CEC) as predictors. Methods: Three sites in northern Germany were intensively monitored with respect to CuAR and SOC contents, pH, and CEC. Data analysis consisted of calibrations using the entire data set and of calibration/validation approaches with and without spiking. Results: There was no consistent temporal trend, so data could be combined for the subsequent regressions. Calibration using the entire data set and calibration/validation after random splitting (i.e., pseudo-independent validation) were successful for mixed-effects and cubist models, with Spearman's rank correlation coefficients rs ranging from 0.83 to 0.91 and low root mean squared errors (RMSEs). Both algorithms included SOC, CEC, and pH as essential predictors, whereas site was important only in the mixed-effects models. Three-fold partitioning of the data according to site to create independent validations was again successful for the respective calibrations, but validation results were variable, with rs ranging from 0.04 to 0.76 and generally high RMSEs. Spiking the calibration samples resulted in generally marked improvements of the validations, with rs ranging from 0.45 to 0.67 and lower RMSEs. Conclusions: Overall, the information provided by SOC, pH, and CEC is beneficial for predicting CuAR contents in a closed population of sites using either mixed-effects or cubist models. However, for a prediction of CuAR dynamics at new sites in the region, spiking is required.
Do lower nitrogen fertilization levels require breeding of different types of cultivars in triticale?
(2022) Neuweiler, Jan E.; Trini, Johannes; Maurer, Hans Peter; Würschum, Tobias
Breeding high-yielding, nitrogen-efficient crops is of utmost importance to achieve greater agricultural sustainability. The aim of this study was to evaluate nitrogen use efficiency (NUE) of triticale, investigate long-term genetic trends and the genetic architecture, and develop strategies for NUE improvement by breeding. For this, we evaluated 450 different triticale genotypes under four nitrogen fertilization levels in multi-environment field trials for grain yield, protein content, starch content and derived indices. Analysis of temporal trends revealed that modern cultivars are better in exploiting the available nitrogen. Genome-wide association mapping revealed a complex genetic architecture with many small-effect QTL and a high level of pleiotropy for NUE-related traits, in line with phenotypic correlations. Furthermore, the effect of some QTL was dependent on the nitrogen fertilization level. High correlations of each trait between N levels and the rather low genotype-by-N-level interaction variance showed that generally the same genotypes perform well over different N levels. Nevertheless, the best performing genotype was always a different one. Thus, selection in early generations can be done under high nitrogen fertilizer conditions as these provide a stronger differentiation, but the final selection in later generations should be conducted with a nitrogen fertilization as in the target environment.
Drought impacts on plant–soil carbon allocation - integrating future mean climatic conditions
(2025) Leyrer, Vinzent; Blum, Juliette; Marhan, Sven; Kandeler, Ellen; Zimmermann, Telse; Berauer, Bernd J.; Schweiger, Andreas H.; Canarini, Alberto; Richter, Andreas; Poll, Christian
Droughts affect soil microbial abundance and functions—key parameters of plant–soil carbon (C) allocation dynamics. However, the impact of drought may be modified by the mean climatic conditions to which the soil microbiome has previously been exposed. In a future warmer and drier world, effects of drought may therefore differ from those observed in studies that simulate drought under current climatic conditions. To investigate this, we used the field experiment ‘Hohenheim Climate Change,’ an arable field where predicted drier and warmer mean climatic conditions had been simulated for 12 years. In April 2021, we exposed this agroecosystem to 8 weeks of drought with subsequent rewetting. Before drought, at peak drought, and after rewetting, we pulse‐labelled winter wheat in situ with 13CO2 to trace recently assimilated C from plants to soil microorganisms and back to the atmosphere. Severe drought decreased soil respiration (−35%) and abundance of gram‐positive bacteria (−15%) but had no effect on gram‐negative bacteria, fungi, and total microbial biomass C. This pattern was not affected by the mean precipitation regime to which the microbes had been pre‐exposed. Reduced mean precipitation had, however, a legacy effect by decreasing the proportion of recently assimilated C allocated to the microbial biomass C pool (−50%). Apart from that, continuous soil warming was an important driver of C fluxes throughout our experiment, increasing plant biomass, root sugar concentration, labile C, and respiration. Warming also shifted microorganisms toward utilizing soil organic matter as a C source instead of recently assimilated compounds. Our study found that moderate shifts in mean precipitation patterns can impose a legacy on how plant‐derived C is allocated in the microbial biomass of a temperate agroecosystem during drought. The overarching effect of soil warming, however, suggests that how temperate agroecosystems respond to drought will mainly be affected by future temperature increases.
Early prediction of biomass in hybrid rye based on hyperspectral data surpasses genomic predictability in less-related breeding material
(2021) Galán, Rodrigo José; Bernal-Vasquez, Angela-Maria; Jebsen, Christian; Piepho, Hans-Peter; Thorwarth, Patrick; Steffan, Philipp; Gordillo, Andres; Miedaner, Thomas
Key message: Hyperspectral data is a promising complement to genomic data to predict biomass under scenarios of low genetic relatedness. Sufficient environmental connectivity between data used for model training and validation is required. Abstract: The demand for sustainable sources of biomass is increasing worldwide. The early prediction of biomass via indirect selection of dry matter yield (DMY) based on hyperspectral and/or genomic prediction is crucial to affordably untap the potential of winter rye (Secale cereale L.) as a dual-purpose crop. However, this estimation involves multiple genetic backgrounds and genetic relatedness is a crucial factor in genomic selection (GS). To assess the prospect of prediction using reflectance data as a suitable complement to GS for biomass breeding, the influence of trait heritability ( ) and genetic relatedness were compared. Models were based on genomic (GBLUP) and hyperspectral reflectance-derived (HBLUP) relationship matrices to predict DMY and other biomass-related traits such as dry matter content (DMC) and fresh matter yield (FMY). For this, 270 elite rye lines from nine interconnected bi-parental families were genotyped using a 10 k-SNP array and phenotyped as testcrosses at four locations in two years (eight environments). From 400 discrete narrow bands (410 nm–993 nm) collected by an uncrewed aerial vehicle (UAV) on two dates in each environment, 32 hyperspectral bands previously selected by Lasso were incorporated into a prediction model. HBLUP showed higher prediction abilities (0.41 – 0.61) than GBLUP (0.14 – 0.28) under a decreased genetic relationship, especially for mid-heritable traits (FMY and DMY), suggesting that HBLUP is much less affected by relatedness and . However, the predictive power of both models was largely affected by environmental variances. Prediction abilities for DMY were further enhanced (up to 20%) by integrating both matrices and plant height into a bivariate model. Thus, data derived from high-throughput phenotyping emerges as a suitable strategy to efficiently leverage selection gains in biomass rye breeding; however, sufficient environmental connectivity is needed.
Historic insights and future potential in wheat elaborated using a diverse cultivars collection and extended phenotyping
(2025) El Hassouni, Khaoula; Afzal, Muhammad; Boeven, Philipp H. G.; Dornte, Jost; Koch, Michael; Pfeiffer, Nina; Pfleger, Franz; Rapp, Matthias; Schacht, Johannes; Spiller, Monika; Sielaff, Malte; Tenzer, Stefan; Thorwarth, Patrick; Longin, C. Friedrich H.
Wheat is one of the most important staple crops worldwide. Wheat breeding mainly focused on improving agronomy and techno-functionality for bread or pasta production, but nutrient content is becoming more important to fight malnutrition. We therefore investigated 282 bread wheat cultivars from seven decades of wheat breeding in Central Europe on 63 different traits related to agronomy, quality and nutrients in multiple field environments. Our results showed that wheat breeding has tremendously increased grain yield, resistance against diseases and lodging as well as baking quality across last decades. By contrast, mineral content slightly decreased without selection on it, probably due to its negative correlation with grain yield. The significant genetic variances determined for almost all traits show the potential for further improvement but significant negative correlations among grain yield and baking quality as well as grain yield and mineral content complicate their combined improvement. Thus, compromises in improvement of these traits are necessary to feed a growing global population.
How many checks are needed per cycle in a plant breeding or variety testing programme?
(2025) Piepho, Hans‐Peter; Laidig, Friedrich
Check varieties are used in plant breeding and variety testing for a number of reasons. One important use of checks is to provide connectivity between years, which facilitates comparison among genotypes of interest that are tested in different years. When long‐term data are available, such comparisons allow an assessment of realized genetic gain (RGG). Here, we consider the question of how many check varieties are needed per cycle for a reliable assessment of RGG. We propose an approach that makes use of variance component estimates for relevant random effects in a linear mixed model and plugs them into an analysis of dummy datasets set up to represent the design options being considered. Our results show that it is useful to employ a larger number of checks and to keep the replacement rate low. Furthermore, there is intercycle information to be recovered, especially when there are few checks and replacement rates are high, so modelling the cycle main effect as random pays off.
Impact of plastic rain shields and exclusion netting on pest dynamics and implications for pesticide use in apples
(2025) Bischoff, Robert; Piepho, Hans-Peter; Scheer, Christian; Petschenka, Georg
Apple production is among the most pesticide-intensive cultures. Recently, plastic rain shields and pest exclusion netting have emerged as potential measures to reduce the heavy reliance on chemical pesticides in apple, due to their inhibitory effect on pathogen and pest infestations. In a field trial, we compared yields, pest, and pathogen abundance in an orchard consisting of four plots, where two plots were covered with anti-hail net covers, one with plastic rain shields only, and one with plastic rain shields and exclusion netting. Pests and pathogens were assessed visually, and beating tray samples were collected to compare overall arthropod diversity between plots. We observed virtually no scab infections in both plastic rain shield plots, despite a more than 70% reduction of fungicides applied, when compared to anti-hail plots. Although no codling moth insecticides were sprayed in the plot with exclusion netting we found significantly reduced damage here, when compared to the anti-hail plots. However, likely due to microclimatic changes, we observed an increase of powdery mildew, woolly apple aphids, and spider mites under plastic rain shields. Modeling of metabolic rates of arthropod herbivores and predators revealed that there is an increased potential of herbivory under plastic rain shields. However, in terms of plant protection, the net effect of plastic rain shields and exclusion netting was a substantial reduction in chemical pesticide use, demonstrating that they represent a promising approach to minimize the use of chemical pesticides in apple production.
Impacts of different light spectra on CBD, CBDA and terpene concentrations in relation to the flower positions of different cannabis Sativa L. strains
(2022) Reichel, Philipp; Munz, Sebastian; Hartung, Jens; Kotiranta, Stiina; Graeff-Hönninger, Simone
Cannabis is one of the oldest cultivated plants, but plant breeding and cultivation are restricted by country-specific regulations. The plant has gained interest due to its medically important secondary metabolites, cannabinoids and terpenes. Besides biotic and abiotic stress factors, secondary metabolism can be manipulated by changing light quality and intensity. In this study, three morphologically different cannabis strains were grown in a greenhouse experiment under three different light spectra with three real light repetitions. The chosen light sources were as follows: a CHD Agro 400 ceramic metal-halide lamp with a sun-like broad spectrum and an R:FR ratio of 2.8, and two LED lamps, a Solray (SOL) and an AP67, with R:FR ratios of 13.49 and 4, respectively. The results of the study indicated that the considered light spectra significantly influenced CBDA and terpene concentrations in the plants. In addition to the different light spectra, the distributions of secondary metabolites were influenced by flower positions. The distributions varied between strains and indicated interactions between morphology and the chosen light spectra. Thus, the results demonstrate that secondary metabolism can be artificially manipulated by the choice of light spectrum, illuminant and intensity. Furthermore, the data imply that, besides the cannabis strain selected, flower position can have an impact on the medicinal potencies and concentrations of secondary metabolites.
Influence of cutting height on biomass yield and quality of miscanthus genotypes
(2021) Magenau, Elena; Kiesel, Andreas; Clifton‐Brown, John; Lewandowski, Iris
Commercially achieved biomass yields are often lower than those obtained in scientific plot trials and estimated by crop models. This phenomenon is commonly referred to as the ‘commercial yield gap’. It needs to be understood and managed to achieve the yield expectations that underpin business models. Cutting height at harvest is one of the key factors determining biomass yield and quality. This study quantifies the impacts of cutting heights of diverse genotypes with different morphologies and in years with contrasting weather conditions before and during harvest. Harvests were made in March 2015 and March 2018 of six diverse miscanthus genotypes planted as part of the ‘OPTIMISC project’ in 2013 near Stuttgart, Germany. Biomass yield, dry matter content and nutrient concentrations were analysed in four 10 cm fractions working upwards from the ground level and a fifth fraction with the shoot biomass higher than 40 cm. As stems are slightly tapered (i.e. diameter decreases slightly with increasing cutting height), it was hypothesized that low cutting may lead to yield gains, but that these may be associated with lower quality biomass with higher moisture and higher nutrient offtakes. We calculated average yield losses of 270 kg ha−1 (0.83%) with each 1 cm increase in cutting height up to 40 cm. Although whole shoot mineral concentrations were significantly influenced by both genotype and year interactions, total nitrogen (1.89 mg g−1), phosphorus (0.51 mg g−1), potassium (3.72 mg g−1) and calcium (0.89 mg g−1) concentrations did not differ significantly from the concentrations in the lower basal sections. Overall, cutting height had a limited influence on nutrient and moisture content. Therefore, we recommend that cutting is performed as low as is practically possible with the available machinery and local ground surface conditions to maximize biomass yield.
Integration of genotypic, hyperspectral, and phenotypic data to improve biomass yield prediction in hybrid rye
(2020) Galán, Rodrigo José; Bernal-Vasquez, Angela-Maria; Jebsen, Christian; Piepho, Hans-Peter; Thorwarth, Patrick; Steffan, Philipp; Gordillo, Andres; Miedaner, Thomas
Integrating cutting-edge technologies is imperative to sustainably breed crops for a growing global population. To predict dry matter yield (DMY) in winter rye (Secale cereale L.), we tested single-kernel models based on genomic (GBLUP) and hyperspectral reflectance-derived (HBLUP) relationship matrices, a multi-kernel model combining both matrices and a bivariate model fitted with plant height as a secondary trait. In total, 274 elite rye lines were genotyped using a 10 k-SNP array and phenotyped as testcrosses for DMY and plant height at four locations in Germany in two years (eight environments). Spectral data consisted of 400 discrete narrow bands ranging between 410 and 993 nm collected by an unmanned aerial vehicle (UAV) on two dates on each environment. To reduce data dimensionality, variable selection of bands was performed, resulting in the least absolute shrinkage and selection operator (Lasso) as the best method in terms of predictive abilities. The mean heritability of reflectance data was moderate ( h2 = 0.72) and highly variable across the spectrum. Correlations between DMY and single bands were generally significant (p < 0.05) but low (≤ 0.29). Across environments and training set (TRN) sizes, the bivariate model showed the highest prediction abilities (0.56–0.75), followed by the multi-kernel (0.45–0.71) and single-kernel (0.33–0.61) models. With reduced TRN, HBLUP performed better than GBLUP. The HBLUP model fitted with a set of selected bands was preferred. Within and across environments, prediction abilities increased with larger TRN. Our results suggest that in the era of digital breeding, the integration of high-throughput phenotyping and genomic selection is a promising strategy to achieve superior selection gains in hybrid rye.
Iron partitioning and photosynthetic performance in Cannabis sativa L. reveal limitations of nanoscale zero-valent iron as a fertilizer
(2025) Büser, Christian; Hartung, Jens; Deurin, Lukas; Graeff-Hönninger, Simone
Iron (Fe) is the fourth most abundant element in the Earth’s crust but remains the third most limiting nutrient for crop productivity due to its low solubility in most soils. The emergence of nanotechnology has introduced nanoscale zero-valent iron (nZVI) as a potential Fe fertilizer with high surface reactivity and improved bioavailability. However, its comparative efficacy relative to conventional chelated Fe sources remains poorly understood. This study investigated Fe partitioning, photosynthetic efficiency, biomass accumulation, and cannabinoid synthesis in Cannabis sativa L. grown hydroponically under Fe-EDTA, nZVI, or Fe-deficient (-Fe) treatments. Total Fe concentrations were markedly reduced in -Fe plants compared with both Fe-EDTA and nZVI treatments. Despite similar root Fe contents between Fe-EDTA and nZVI, only Fe-EDTA facilitated efficient translocation to shoots, while nZVI-derived Fe predominantly accumulated in roots. Consequently, nZVI-treated plants exhibited intermediate photosynthetic performance and water-use efficiency—lower than Fe-EDTA but significantly higher than -Fe. Although Fe translocation differed substantially, inflorescence biomass and cannabinoid yield were comparable between nZVI and Fe-EDTA treatments, both exceeding those of -Fe plants. These results suggest that yield reductions under Fe deficiency arise not solely from Fe scarcity but also from the metabolic costs of Strategy I Fe acquisition, which are partially circumvented by root Fe availability from nZVI. Overall, Fe-EDTA demonstrated superior nutrient use efficiency, whereas nZVI partially alleviated Fe deficiency and revealed distinctive interactions between nanomaterials and plant Fe physiology. This study advances understanding of nZVI as an alternative Fe source in C. sativa and provides new insights into nanoparticle–plant nutrient dynamics.
Lentils can absorb amino acids as a nitrogen source supporting early growth
(2025) Kröper, Alex A.; Zikeli, Sabine; Wimmer, Monika A.; Zörb, Christian
Background: Lentils ( Lens culinaris Medik.) are a valuable crop due to their high nutritional content, low environmental impact, and nitrogen‐fixing ability via rhizobacteria. Early in development, before this symbiosis is established, lentils require external nitrogen, typically supplied through fertilizers or already present in soils. Aim: This study explores whether lentils can utilize amino acids as a nitrogen source and how amino acid supplementation affects growth and nitrate uptake. Results: The findings show that lentils can absorb amino acids from soil, with no adverse effects on growth compared to mineral N fertilizers. The amino acid patterns show only slight changes in individual amino acids. NPF/NRT1, NRT2, AMT2, and DUR3 were expressed in all treatments in root tissue. LHT1 plays a minor role in the distribution of N in the shoots of lentil plants. Conclusion: Although amino acid uptake is less efficient than that of nitrate, it may still benefit young plants in organic farming until rhizobacterial symbiosis is established.

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