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.
Buckwheat in Germany: The effect of variety and sowing date on agronomic traits
(2025) Grimes, Samantha J.; Afzal, Muhammad; Tako, Rea; Hahn, Volker; Graeff‐Hönninger, Simone; Longin, C. Friedrich H.; Grimes, Samantha J.; Department of Agronomy, Institute of Crop Science, University of Hohenheim, Stuttgart, Germany; Afzal, Muhammad; State Plant Breeding Institute, University of Hohenheim, Stuttgart, Germany; Tako, Rea; State Plant Breeding Institute, University of Hohenheim, Stuttgart, Germany; Hahn, Volker; State Plant Breeding Institute, University of Hohenheim, Stuttgart, Germany; Graeff‐Hönninger, Simone; Department of Agronomy, Institute of Crop Science, University of Hohenheim, Stuttgart, Germany; Longin, C. Friedrich H.; State Plant Breeding Institute, University of Hohenheim, Stuttgart, Germany
Buckwheat (Fagopyrum esculentum Moench) requires minimal agrochemical inputs and delivers grains with a high nutritional profile—the perfect prerequisites for future sustainable farming. However, it is currently consumed and produced in only a few countries. The aim of this study was to investigate the potential to successfully grow buckwheat in Germany and to elaborate first insights for local breeding. Therefore, a total of 33 buckwheat varieties were tested across three locations, 3 years, and two different sowing dates. The average yield was 2.3 t ha −1 , ranging from 1.4 to 3.1 t ha −1 across varieties. Similar yields were observed for both early and late sowing dates, and across all tested varieties. All but two of the very late‐maturing common buckwheat varieties could be safely harvested in all locations also on the late sowing date. Key prerequisites to establish local breeding were met, including large genetic variation and high heritability for important agronomic traits. In summary, this study highlights the importance of variety selection and targeted breeding focusing on early‐maturing buckwheat varieties, paving the way for potential double‐cropping systems in Germany that use buckwheat as a second crop and significantly enhance its profitability for farmers.
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.
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.
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.
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.
Generic optimization approach of soil hydraulic parameters for site-specific model applications
(2024) Trenz, Jonas; Memic, Emir; Batchelor, William D.; Graeff-Hönninger, Simone
Site-specific crop management is based on the postulate of varying soil and crop requirements in a field. Therefore, a field is separated into homogenous management zones, using available data to adapt management practices environment to maximize productivity and profitability while reducing environmental impacts. Due to advancing sensor technologies, crop growth and yield data on more minor scales are common, but soil data often needs to be more appropriate. Crop growth models have shown promise as a decision support tool for site-specific farming. The Decision Support System for Agrotechnology Transfer (DSSAT) is a widely used point-based model. To overcome the problem of inappropriate soil input data problem, this study introduces an external plug-in program called Soil Profile Optimizer (SPO), which uses the current DSSAT v4.8 to calibrate soil profile parameters on a site-specific level. Developed as an inverse modelling approach, the SPO can calibrate selected soil profile parameters by targeting available in-season plant data. Root Mean Square Error (RMSE) and normalized RMSE as error minimization criteria are used. The SPO was tested and evaluated by comparing different simulation scenarios in a case study of a 3-yr field trial with maize. The scenario with optimized soil profiles, conducted with the SPO, resulted in an R 2 of 0.76 between simulated and observed yield and led to significant improvements compared to the scenario conducted with field scale soil profile information (R 2 0.03). The SPO showed promise in using spatial plant measurements to estimate management zone scale soil parameters required for the DSSAT model.
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 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.
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 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.

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