Institut für Pflanzenzüchtung, Saatgutforschung und Populationsgenetik

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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.
DeepCob: precise and high-throughput analysis of maize cob geometry using deep learning with an application in genebank phenomics
(2021) Kienbaum, Lydia; Correa Abondano, Miguel; Blas, Raul; Schmid, Karl
Background: Maize cobs are an important component of crop yield that exhibit a high diversity in size, shape and color in native landraces and modern varieties. Various phenotyping approaches were developed to measure maize cob parameters in a high throughput fashion. More recently, deep learning methods like convolutional neural networks (CNNs) became available and were shown to be highly useful for high-throughput plant phenotyping. We aimed at comparing classical image segmentation with deep learning methods for maize cob image segmentation and phenotyping using a large image dataset of native maize landrace diversity from Peru. Results: Comparison of three image analysis methods showed that a Mask R-CNN trained on a diverse set of maize cob images was highly superior to classical image analysis using the Felzenszwalb-Huttenlocher algorithm and a Window-based CNN due to its robustness to image quality and object segmentation accuracy (r = 0.99). We integrated Mask R-CNN into a high-throughput pipeline to segment both maize cobs and rulers in images and perform an automated quantitative analysis of eight phenotypic traits, including diameter, length, ellipticity, asymmetry, aspect ratio and average values of red, green and blue color channels for cob color. Statistical analysis identified key training parameters for efficient iterative model updating. We also show that a small number of 10–20 images is sufficient to update the initial Mask R-CNN model to process new types of cob images. To demonstrate an application of the pipeline we analyzed phenotypic variation in 19,867 maize cobs extracted from 3449 images of 2484 accessions from the maize genebank of Peru to identify phenotypically homogeneous and heterogeneous genebank accessions using multivariate clustering. Conclusions: Single Mask R-CNN model and associated analysis pipeline are widely applicable tools for maize cob phenotyping in contexts like genebank phenomics or plant breeding.
Dwarfing gene Rht24 does not affect Fusarium head blight resistance in a large European winter wheat diversity panel
(2022) Miedaner, Thomas; Lenhardt, Melissa; Grehl, Janosch; Gruner, Paul; Koch, Silvia
Reduced height ( Rht ) genes are widely used in modern wheat breeding although some confer higher susceptibility to Fusarium head blight (FHB) caused by F. graminearum and other species. Our objective was to test whether the recently identified Rht24b dwarfing allele has a neutral effect on FHB response as reported previously from a single mapping population when unrelated winter wheat cultivars were analyzed. We artificially infected a panel of 420 cultivars divided into four genotypic groups ( Rht24a + Rht-D1a , Rht24b + Rht-D1a, Rht24a + Rht-D1b, Rht24b + Rht-D1b ) with Fusarium culmorum in five location-year combinations. High and significant (P ≤ 0.001) genetic variance for FHB severity and plant height (PH) was found in the entire panel as well as within the four Rht groups and both traits showed high entry-mean heritabilities of 0.92 and 0.98, respectively. Rht24b had no significant effect on FHB severity whereas Rht-D1b increased FHB susceptibility by 37%. The 29 most resistant cultivars either had the tallness alleles of the above mentioned Rht-D1 gene or Rht24b alone. The Rht24b + Rht-D1b combination had no significantly higher FHB severity than Rht-D1b alone. However, Rht24b reduced average PH only by 6.8 cm, whereas Rht-D1b conferred a reduction of 13.6 cm. For breeding short, FHB-resistant germplasm the neutral Rht24 gene must be complemented by further QTL or other FHB-neutral Rht genes.
Effects of elevated atmospheric CO2 and its interaction with temperature and nitrogen on yield of barley (Hordeum vulgare L.): a meta-analysis
(2022) Gardi, Mekides Woldegiorgis; Haussmann, Bettina I. G.; Malik, Waqas Ahmed; Högy, Petra
Aims: The general aim of this meta-analysis is to synthesize and summarize the mean response of barley yield variables to elevated CO2 (eCO2) and how temperature and nitrogen (N) affect the CO2-induced yield responses of barley. Methods: A meta-analysis procedure was used to analyze five yield variables of barley extracted from 22 studies to determine the effect size and the magnitude concerning eCO2 and its interaction with temperature and N. Results: CO2 enrichment increased aboveground biomass (23.8%), grain number (24.8%), and grain yield (27.4%). The magnitude of the responses to eCO2 was affected by genotype, temperature, nitrogen, and CO2 exposure methods. Genotype “Anakin” shows the highest CO2 response of aboveground biomass (47.1%), while “Bambina” had the highest grain number (58.4%). Grain yield response was observed to be higher for genotypes “Alexis” (38.1%) and “Atem” (33.7%) under eCO2. The increase of aboveground biomass and grain yield was higher when plants were grown under eCO2 in combination with higher N (151–200 kg ha−1). The interaction between eCO2 and three different temperature levels was analyzed to identify the impacts on barley yield components. The results revealed that the CO2-induced increase in grain number and grain yield was higher in combination with a temperature level of 21–25 °C as compared to lower levels (< 15 and 16–20 ℃). The response of barley yield to eCO2 was higher in growth chambers than in other CO2 exposure methods. Moreover, a higher response of aboveground biomass and grain yield to eCO2 was observed for pot-grown plants compared to field-grown.ConclusionsOverall, results suggest that the maximal barley production under eCO2 will be obtained in combination with high N fertilizer and temperature levels (21–25 °C).
Effects of using deep learning to predict the geographic origin of barley genebank accessions on genome–environment association studies
(2025) Chang, Che-Wei; Schmid, Karl
Genome–environment association (GEA) is an approach for identifying adaptive loci by combining genetic variation with environmental parameters, offering potential for improving crop resilience. However, its application to genebank accessions is limited by missing geographic origin data. To address this limitation, we explored the use of neural networks to predict the geographic origins of barley accessions and integrate imputed environmental data into GEA. Neural networks demonstrated high accuracy in cross-validation but occasionally produced ecologically implausible predictions as models solely considered geographical proximity. For example, some predicted origins were located within non-arable regions, such as the Mediterranean Sea. Using barley flowering time genes as benchmarks, GEA integrating imputed environmental data ( N=11,032) displayed partially concordant yet complementary detection of genomic regions near flowering time genes compared to regular GEA ( N=1,626), highlighting the potential of GEA with imputed data to complement regular GEA in uncovering novel adaptive loci. Also, contrary to our initial hypothesis anticipating a significant improvement in GEA performance by increasing sample size, our simulations yield unexpected insights. Our study suggests potential limitations in the sensitivity of GEA approaches to the considerable expansion in sample size achieved through predicting missing geographical data. Overall, our study provides insights into leveraging incomplete geographical origin data by integrating deep learning with GEA. Our findings indicate the need for further development of GEA approaches to optimize the use of imputed environmental data, such as incorporating regional GEA patterns instead of solely focusing on global associations between allele frequencies and environmental gradients across large-scale landscapes.
Exploiting genetic diversity in two European maize landraces for improving Gibberella ear rot resistance using genomic tools
(2021) Gaikpa, David Sewordor; Kessel, Bettina; Presterl, Thomas; Ouzunova, Milena; Galiano-Carneiro, Ana L.; Mayer, Manfred; Melchinger, Albrecht E.; Schön, Chris-Carolin; Miedaner, Thomas
Fusarium graminearum (Fg) causes Gibberella ear rot (GER) in maize leading to yield reduction and contamination of grains with several mycotoxins. This study aimed to elucidate the molecular basis of GER resistance among 500 doubled haploid lines derived from two European maize landraces, “Kemater Landmais Gelb” (KE) and “Petkuser Ferdinand Rot” (PE). The two landraces were analyzed individually using genome-wide association studies and genomic selection (GS). The lines were genotyped with a 600-k maize array and phenotyped for GER severity, days to silking, plant height, and seed-set in four environments using artificial infection with a highly aggressive Fg isolate. High genotypic variances and broad-sense heritabilities were found for all traits. Genotype-environment interaction was important throughout. The phenotypic (r) and genotypic (rg) correlations between GER severity and three agronomic traits were low (r= − 0.27 to 0.20; rg = − 0.32 to 0.22). For GER severity, eight QTLs were detected in KE jointly explaining 34% of the genetic variance. In PE, no significant QTLs for GER severity were detected. No common QTLs were found between GER severity and the three agronomic traits. The mean prediction accuracies (p) of weighted GS (wRR-BLUP) were higher than p of marker-assisted selection (MAS) and unweighted GS (RR-BLUP) for GER severity. Using KE as the training set and PE as the validation set resulted in very low p that could be improved by using fixed marker effects in the GS model.
Feature engineering and parameter tuning: improving phenomic prediction ability in multi-environmental durum wheat breeding trials
(2024) Meyenberg, Carina; Braun, Vincent; Longin, Carl Friedrich Horst; Thorwarth, Patrick
The success of plant breeding programs depends on efficient selection decisions. Phenomic selection has been proposed as a tool to predict phenotype performance based on near-infrared spectra (NIRS) to support selection decisions. In this study, we test the performance of phenomic selection in multi-environmental trials from our durum wheat breeding program for three breeding scenarios and use feature engineering as well as parameter tuning to improve the phenomic prediction ability. In addition, we investigate the influence of genotype and environment on the phenomic prediction ability for agronomic and quality traits. Preprocessing, based on a grid search over the Savitzky–Golay filter parameters based on 756,000 genotype best linear unbiased estimate (BLUE) computations, improved the phenomic prediction ability by up to 1500% (0.02–0.3). Furthermore, we show that preprocessing should be optimized depending on the dataset, trait, and model used for prediction. The phenomic prediction scenarios in our durum breeding program resulted in low-to-moderate prediction abilities with the highest and most stable prediction results when predicting new genotypes in the same environment as used for model training. This is consistent with the finding that NIRS capture both the genotype and genotype-by-environment (G×E)interaction variance.
Genetic architecture underlying the expression of eight α-amylase trypsin inhibitors
(2021) El Hassouni, Khaoula; Sielaff, Malte; Curella, Valentina; Neerukonda, Manjusha; Leiser, Willmar; Würschum, Tobias; Schuppan, Detlef; Tenzer, Stefan; Longin, C. Friedrich H.
Amylase trypsin inhibitors (ATIs) are important allergens in baker’s asthma and suspected triggers of non-celiac wheat sensitivity (NCWS) inducing intestinal and extra-intestinal inflammation. As studies on the expression and genetic architecture of ATI proteins in wheat are lacking, we evaluated 149 European old and modern bread wheat cultivars grown at three different field locations for their content of eight ATI proteins. Large differences in the content and composition of ATIs in the different cultivars were identified ranging from 3.76 pmol for ATI CM2 to 80.4 pmol for ATI 0.19, with up to 2.5-fold variation in CM-type and up to sixfold variation in mono/dimeric ATIs. Generally, heritability estimates were low except for ATI 0.28 and ATI CM2. ATI protein content showed a low correlation with quality traits commonly analyzed in wheat breeding. Similarly, no trends were found regarding ATI content in wheat cultivars originating from numerous countries and decades of breeding history. Genome-wide association mapping revealed a complex genetic architecture built of many small, few medium and two major quantitative trait loci (QTL). The major QTL were located on chromosomes 3B for ATI 0.19-like and 6B for ATI 0.28, explaining 70.6 and 68.7% of the genotypic variance, respectively. Within close physical proximity to the medium and major QTL, we identified eight potential candidate genes on the wheat reference genome encoding structurally related lipid transfer proteins. Consequently, selection and breeding of wheat cultivars with low ATI protein amounts appear difficult requiring other strategies to reduce ATI content in wheat products.
Genetic dissection of drought tolerance in maize through GWAS of agronomic traits, stress tolerance indices, and phenotypic plasticity
(2025) Li, Ronglan; Li, Dongdong; Guo, Yuhang; Wang, Yueli; Zhang, Yufeng; Li, Le; Yang, Xiaosong; Chen, Shaojiang; Würschum, Tobias; Liu, Wenxin; Han, De-Guo
Drought severely limits crop yield every year, making it critical to clarify the genetic basis of drought tolerance for breeding of improved varieties. As drought tolerance is a complex quantitative trait, we analyzed three phenotypic groups: (1) agronomic traits under well-watered (WW) and water-deficit (WD) conditions, (2) stress tolerance indices of these traits, and (3) phenotypic plasticity, using a multi-parent doubled haploid (DH) population assessed in multi-environment trials. Genome-wide association studies (GWAS) identified 130, 171, and 71 quantitative trait loci (QTL) for the three groups of phenotypes, respectively. Only one QTL was shared among all trait groups, 25 between stress indices and agronomic traits, while the majority of QTL were specific to their group. Functional annotation of candidate genes revealed distinct pathways of the three phenotypic groups. Candidate genes under WD conditions were enriched for stress response and epigenetic regulation, while under WW conditions for protein synthesis and transport, RNA metabolism, and developmental regulation. Stress tolerance indices were enriched for transport of amino/organic acids, epigenetic regulation, and stress response, whereas plasticity showed enrichment for environmental adaptability. Transcriptome analysis of 26 potential candidate genes showed tissue-specific drought responses in leaves, ears, and tassels. Collectively, these results indicated both shared and independent genetic mechanisms underlying drought tolerance, providing novel insights into the complex phenotypes related to drought tolerance and guiding further strategies for molecular breeding in maize.
Genome-wide association study for in vitro digestibility and related traits in triticale forage
(2024) De Zutter, Anneleen; Piro, Maria Chiara; Maenhout, Steven; Maurer, Hans Peter; De Boever, Johan; Muylle, Hilde; Roldán-Ruiz, Isabel; Haesaert, Geert
Background: Triticale is making its way on dairy farms as an alternative forage crop. This requires the availability of high-yielding triticale varieties with good digestibility. Triticale forage breeding mainly focussed on biomass yield, but efforts to improve digestibility are increasing. We previously investigated the interrelationships among different quality traits in soft dough triticale: starch, acid detergent fibre and in vitro digestibility of organic matter (IVOMD) and of neutral detergent fibre (IVNDFD) of the total plant, IVNDFD and Klason lignin of the stems, and ear proportion and stem length. Here we determine the genetic control of these traits, using a genome-wide association (GWAS) approach. A total of 33,231 DArTseq SNP markers assessed in a collection of 118 winter triticale genotypes, including 101 varieties and 17 breeding lines, were used. Results: The GWAS identified a total of 53 significant marker-trait associations (MTAs). The highest number of significantly associated SNP markers (n = 10) was identified for total plant IVNDFD. A SNP marker on chromosome 1A (4211801_19_C/T; 474,437,796 bp) was found to be significantly associated with ear proportion, and plant and stem IVNDFD, with the largest phenotypic variation for ear proportion (R²p = 0.23). Based on MTAs, candidate genes were identified which were of particular relevance for variation in in vitro digestibility (IVD) because they are putatively involved in plasma membrane transport, cytoskeleton organisation, carbohydrate metabolic processes, protein phosphorylation, and sterol and cell wall biogenesis. Interestingly, a xyloglucan-related candidate gene on chromosome 2R, SECCE2Rv1G0126340, was located in close proximity of a SNP significantly associated with stem IVNDFD. Furthermore, quantitative trait loci previously reported in wheat co-localized with significantly associated SNP markers in triticale. Conclusions: A collection of 118 winter triticale genotypes combined with DArTseq SNP markers served as a source for identifying 53 MTAs and several candidate genes for forage IVD and related traits through a GWAS approach. Taken together, the results of this study demonstrate that the genetic diversity available in this collection can be further exploited for research and breeding purposes to improve the IVD of triticale forage.
Genome-wide association study for resistances to yellow rust, powdery mildew, and Septoria tritici blotch in cultivated emmer
(2024) Miedaner, T.; Afzal, M.; Longin, C. F.
Emmer is a progenitor of bread wheat and evolved in the Levant together with the yellow rust (YR), powdery mildew (PM) fungi, and a precursor of Zymoseptoria tritici causing Septoria tritici blotch (STB). We performed a genome-wide association mapping for the three disease resistances with 143 cultivated emmer accessions in multi-environmental trials. Significant (P < 0.001) genotypic variation was found with high heritabilities for the resistances to the two biotrophs and a moderate heritability for STB resistance. For YR, PM, and STB severity nine, three, and seven marker-trait associations, respectively, were detected that were significant across all environments. Most of them were of low to moderate effect, but for PM resistance a potentially new major gene was found on chromosome 7AS. Genomic prediction abilities were high throughout for all three resistances (≥ 0.8) and decreased only slightly for YR and PM resistances when the prediction was done for the second year with the first year as training set (≥ 0.7). For STB resistance prediction ability was much lower in this scenario (0.4). Despite this, genomic selection should be advantageous given the large number of small QTLs responsible for quantitative disease resistances. A challenge for the future is to combine these multiple disease resistances with better lodging tolerance and higher grain yield.
Genomic prediction in hybrid breeding: I. Optimizing the training set design
(2023) Melchinger, Albrecht E.; Fernando, Rohan; Stricker, Christian; Schön, Chris-Carolin; Auinger, Hans-Jürgen
Genomic prediction holds great promise for hybrid breeding but optimum composition of the training set (TS) as determined by the number of parents (nTS) and crosses per parent (c) has received little attention. Our objective was to examine prediction accuracy (ra) of GCA for lines used as parents of the TS (I1 lines) or not (I0 lines), and H0, H1 and H2 hybrids, comprising crosses of type I0 × I0, I1 × I0 and I1 × I1, respectively, as function of nTS and c. In the theory, we developed estimates for ra of GBLUPs for hybrids: (i)r^a based on the expected prediction accuracy, and (ii) r~a based on ra of GBLUPs of GCA and SCA effects. In the simulation part, hybrid populations were generated using molecular data from two experimental maize data sets. Additive and dominance effects of QTL borrowed from literature were used to simulate six scenarios of traits differing in the proportion (τSCA = 1%, 6%, 22%) of SCA variance in σG2 and heritability (h2 = 0.4, 0.8). Values of r~a and r^a closely agreed with ra for hybrids. For given size NTS = nTS × c of TS, ra of H0 hybrids and GCA of I0 lines was highest for c = 1. Conversely, for GCA of I1 lines and H1 and H2 hybrids, c = 1 yielded lowest ra with concordant results across all scenarios for both data sets. In view of these opposite trends, the optimum choice of c for maximizing selection response across all types of hybrids depends on the size and resources of the breeding program.
Hybrid transcriptome sequencing approach improved assembly and gene annotation in Cynara cardunculus (L.)
(2020) Puglia, Giuseppe D.; Prjibelski, Andrey D.; Vitale, Domenico; Bushmanova, Elena; Schmid, Karl J.; Raccuia, Salvatore A.
Background: The investigation of transcriptome profiles using short reads in non-model organisms, which lack of well-annotated genomes, is limited by partial gene reconstruction and isoform detection. In contrast, long-reads sequencing techniques revealed their potential to generate complete transcript assemblies even when a reference genome is lacking. Cynara cardunculus var. altilis (DC) (cultivated cardoon) is a perennial hardy crop adapted to dry environments with many industrial and nutraceutical applications due to the richness of secondary metabolites mostly produced in flower heads. The investigation of this species benefited from the recent release of a draft genome, but the transcriptome profile during the capitula formation still remains unexplored. In the present study we show a transcriptome analysis of vegetative and inflorescence organs of cultivated cardoon through a novel hybrid RNA-seq assembly approach utilizing both long and short RNA-seq reads. Results: The inclusion of a single Nanopore flow-cell output in a hybrid sequencing approach determined an increase of 15% complete assembled genes and 18% transcript isoforms respect to short reads alone. Among 25,463 assembled unigenes, we identified 578 new genes and updated 13,039 gene models, 11,169 of which were alternatively spliced isoforms. During capitulum development, 3424 genes were differentially expressed and approximately two-thirds were identified as transcription factors including bHLH, MYB, NAC, C2H2 and MADS-box which were highly expressed especially after capitulum opening. We also show the expression dynamics of key genes involved in the production of valuable secondary metabolites of which capitulum is rich such as phenylpropanoids, flavonoids and sesquiterpene lactones. Most of their biosynthetic genes were strongly transcribed in the flower heads with alternative isoforms exhibiting differentially expression levels across the tissues. Conclusions: This novel hybrid sequencing approach allowed to improve the transcriptome assembly, to update more than half of annotated genes and to identify many novel genes and different alternatively spliced isoforms. This study provides new insights on the flowering cycle in an Asteraceae plant, a valuable resource for plant biology and breeding in Cynara and an effective method for improving gene annotation.
Hybrid wheat: quantitative genetic parameters and heterosis for quality and rheological traits as well as baking volume
(2022) Schwarzwälder, Lea; Thorwarth, Patrick; Zhao, Yusheng; Reif, Jochen Christoph; Longin, C. Friedrich H.
Bread wheat cultivars have been selected according to numerous quality traits to fulfill the requirements of the bread making industry. These include beside protein content and quality also rheological traits and baking volume. We evaluated 35 male and 73 female lines and 119 of their single-cross hybrids at three different locations for grain yield, protein content, sedimentation value, extensograph traits and baking volume. No significant differences ( p < 0.05) were found in the mean comparisons of males, females and hybrids, except for higher grain yield and lower protein content in the hybrids. Mid-parent and better-parent heterosis values were close to zero and slightly negative, respectively, for baking volume and extensograph traits. However, the majority of heterosis values resulted in the finding that hybrids had higher grain yield than lines for a given level of baking volume, sedimentation value or energy value of extensograph. Due to the high correlation with the mid-parent values ( r > 0.70), an initial prediction of hybrid performance based on line per se performance for protein content, sedimentation value, most traits of the extensograph and baking volume is possible. The low variance due to specific combining ability effects for most quality traits points toward an additive gene action requires quality selection within both heterotic groups. Consequently, hybrid wheat can combine high grain yield with high bread making quality. However, the future use of wheat hybrids strongly depends on the establishment of a cost-efficient and reliable seed production system.
Maternal differences for the reaction to ergot in unfertilized hybrid rye (Secale cereale)
(2022) Kodisch, Anna; Schmiedchen, Brigitta; Eifler, Jakob; Gordillo, Andres; Siekmann, Dörthe; Fromme, Franz Joachim; Oberforster, Michael; Miedaner, Thomas
Claviceps purpurea causing ergot maintains to be a problem in commercial cytoplasmic male sterile (CMS)-based hybrid rye growing. The fungal spores compete with pollen during flowering and ergot incidence is reduced in highly pollen-shedding stands. This study was carried out to identify maternal differences in ergot infection in the absence of pollen. Ten male-sterile single crosses were tested by needle and spray inoculation and kept unfertilized in up to four field sites (Germany, Austria) and three greenhouse experiments, respectively, in two years. A medium to high correlation was observed between field (needle inoculation) and greenhouse (spray inoculation) experiments. The environments (=location × year combinations) differed in their ergot severity and ergot incidence. Significant ( P ≤ 0.05) genotypic and genotype × environment interaction variances were detected for the unfertilized male-sterile single crosses in both test systems for both traits. The single cross K_4 showed a significantly lower ergot severity averaged across all environments, thus being more resilient to ergot than the other genotypes. In conclusion, spray and needle inoculation are suitable for testing unfertilized male-sterile rye materials, testing across several environments (locations, years) is definitely necessary. Selection of specific females might give the potential for further reducing ergot contamination in hybrid rye in future. The frequency of such genotypes within larger breeding populations needs to be analyzed.
Maximization through optimization? On the relationship between hybrid performance and parental genetic distance
(2023) Würschum, Tobias; Zhu, Xintian; Zhao, Yusheng; Jiang, Yong; Reif, Jochen C.; Maurer, Hans Peter
Heterosis is the improved performance of hybrids compared with their parental components and is widely exploited in agriculture. According to quantitative genetic theory, genetic distance between parents at heterotic quantitative trait loci is required for heterosis, but how heterosis varies with genetic distance has remained elusive, despite intensive research on the topic. Experimental studies have often found a positive association between heterosis and genetic distance that, however, varied in strength. Most importantly, it has remained unclear whether heterosis increases continuously with genetic distance or whether there is an optimum genetic distance after which heterosis declines again. Here, we revisit the relationship between heterosis and genetic distance and provide perspectives on how to maximize heterosis and hybrid performance in breeding, as well as the consequences for the design of heterotic groups and the utilization of more exotic material and genetic resources.
Optimizing selection based on BLUPs or BLUEs in multiple sets of genotypes differing in their population parameters
(2024) Melchinger, Albrecht E.; Fernando, Rohan; Melchinger, Andreas J.; Schön, Chris-Carolin
Plant breeding programs typically involve multiple families from either the same or different populations, varying in means, genetic variances and prediction accuracy of BLUPs or BLUEs for true genetic values (TGVs) of candidates. We extend the classical breeder's equation for truncation selection from single to multiple sets of genotypes, indicating that the expected overall selection response for TGVs depends on the selection response within individual sets and their post-selection proportions. For BLUEs, we show that maximizing requires thresholds optimally tailored for each set, contingent on their population parameters. For BLUPs, we prove that is maximized by applying a uniform threshold across all candidates from all sets. We provide explicit formulas for the origin of the selected candidates from different sets and show that their proportions before and after selection can differ substantially, especially for sets with inferior properties and low proportion. We discuss implications of these results for (a) optimum allocation of resources to training and prediction sets and (b) the need to counteract narrowing the genetic variation under genomic selection. For genomic selection of hybrids based on BLUPs of GCA of their parent lines, selecting distinct proportions in the two parent populations can be advantageous, if these differ substantially in the variance and/or prediction accuracy of GCA. Our study sheds light on the complex interplay of selection thresholds and population parameters for the selection response in plant breeding programs, offering insights into the effective resource management and prudent application of genomic selection for improved crop development.
Optimum breeding strategies using genomic and phenotypic selection for the simultaneous improvement of two traits
(2021) Marulanda, Jose J.; Mi, Xuefei; Utz, H. Friedrich; Melchinger, Albrecht E.; Würschum, Tobias; Longin, C. Friedrich H.
Selection indices using genomic information have been proposed in crop-specific scenarios. Routine use of genomic selection (GS) for simultaneous improvement of multiple traits requires information about the impact of the available economic and logistic resources and genetic properties (variances, trait correlations, and prediction accuracies) of the breeding population on the expected selection gain. We extended the R package “selectiongain” from single trait to index selection to optimize and compare breeding strategies for simultaneous improvement of two traits. We focused on the expected annual selection gain (ΔGa) for traits differing in their genetic correlation, economic weights, variance components, and prediction accuracies of GS. For all scenarios considered, breeding strategy GSrapid (one-stage GS followed by one-stage phenotypic selection) achieved higher ΔGa than classical two-stage phenotypic selection, regardless of the index chosen to combine the two traits and the prediction accuracy of GS. The Smith–Hazel or base index delivered higher ΔGa for net merit and individual traits compared to selection by independent culling levels, whereas the restricted index led to lower ΔGa in net merit and divergent results for selection gain of individual traits. The differences among the indices depended strongly on the correlation of traits, their variance components, and economic weights, underpinning the importance of choosing the selection indices according to the goal of the breeding program. We demonstrate our theoretical derivations and extensions of the R package “selectiongain” with an example from hybrid wheat by designing indices to simultaneously improve grain yield and grain protein content or sedimentation volume.
Order from entropy: big data from FAIR data cohorts in the digital age of plant breeding
(2025) Gogna, Abhishek; Arend, Daniel; Beier, Sebastian; Rezaei, Ehsan Eyshi; Würschum, Tobias; Zhao, Yusheng; Chu, Jianting; Reif, Jochen C.
Lack of interoperable datasets in plant breeding research creates an innovation bottleneck, requiring additional effort to integrate diverse datasets—if access is possible at all. Handling of plant breeding data and metadata must, therefore, change toward adopting practices that promote openness, collaboration, standardization, ethical data sharing, sustainability, and transparency of provenance and methodology. FAIR Digital Objects, which build on research data infrastructures and FAIR principles, offer a path to address this interoperability crisis, yet their adoption remains in its infancy. In the present work, we identify data sharing practices in the plant breeding domain as Data Cohorts and establish their connection to FAIR Digital Objects. We further link these cohorts to broader research infrastructures and propose a Data Trustee model for federated data sharing. With this we aim to push the boundaries of data management, often viewed as the last step in plant breeding research, to an ongoing process to enable future innovations in the field.
Participatory research at scale: a comparative analysis of four approaches to large-scale agricultural technology testing with farmers
(2024) Oberson, Nathalie; Moussa, Hannatou O; Aminou, Ali M; Kidane, Yosef Gebrehawaryat; Luo, Juliet Nangamba; Giuliani, Alessandra; Weltzien, Eva; Haussmann, Bettina IG
Tailoring agricultural technology options to the diverse conditions of smallholder farmers requires innovative approaches for testing these technologies with farmers across varied contexts, while incorporating their feedback into learning and decision-making processes. This study compares four such approaches: the Farmer Field School on Participatory Plant Breeding (FFS-PPB), Farmer Research Network (FRN), Crowdsourcing–Triadic comparisons of technologies (Tricot), and adapted Mother–Baby Trial (MBT) as implemented by four concrete projects. The objectives are to provide detailed descriptions of these approaches and their project-specific implementations, identify and analyze common aspects and differences, and derive insights to guide future farmer-inclusive projects aiming at contextual scaling of agricultural technologies. A literature review, key informant interviews, and a systematic content analysis were conducted for the analysis. Common features include cascade training models, simple farmer-managed experiments, and the use of digital tools for data collection. Major differences lie in the extent of farmer–researcher collaboration and decision-making, as well as how technology option-by-context interactions are addressed. The FRN, FFS-PPB, and adapted MBT approaches involve farmers in decision-making throughout most stages of research, including co-learning cycles that adapt the research design and technology options to farmers’ needs. Although these approaches require more training and expertise, they increase the likelihood of achieving relevant results that farmers can implement in practice. In contrast, more standardized approaches like the Crowdsourcing–Tricot streamline the implementation, data management and analysis of large-scale trials, but have limitations in capturing the underlying reasons for farmers’ preferences. Among the studied approaches, the FRN as implemented by the Women's Fields project in Niger is particularly effective in identifying which options best suit specific farming contexts.

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