Hans-Peter Piepho

Hohenheim University, Stuttgart, Baden-Württemberg, Germany

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Publications (127)291.28 Total impact

  • Emlyn R. Williams, Hans-Peter Piepho
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    ABSTRACT: Designs exhibiting super-valid restricted randomization have been proposed as alternatives to efficient row–column designs in situations where the column variance component is small. This paper examines some existing uniformity data and results from two field variety trial programs and concludes that in these situations efficient row–column designs are to be preferred.
    Journal of Agricultural Biological and Environmental Statistics 12/2014; · 1.24 Impact Factor
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    ABSTRACT: Maize (Zea mays) displays an exceptional level of structural genomic diversity, which is likely unique among higher eukaryotes. In this study, we surveyed how the genetic divergence of two maize inbred lines affects the transcriptomic landscape in four different primary root tissues of their F1-hybrid progeny. An extreme instance of complementation was frequently observed: genes that were expressed in only one parent but in both reciprocal hybrids. This single-parent expression (SPE) pattern was detected for 2341 genes with up to 1287 SPE patterns per tissue. As a consequence, the number of active genes in hybrids exceeded that of their parents in each tissue by >400. SPE patterns are highly dynamic, as illustrated by their excessive degree of tissue specificity (80%). The biological significance of this type of complementation is underpinned by the observation that a disproportionally high number of SPE genes (75 to 82%) is nonsyntenic, as opposed to all expressed genes (36%). These genes likely evolved after the last whole-genome duplication and are therefore younger than the syntenic genes. In summary, SPE genes shape the remarkable gene expression plasticity between root tissues and complementation in maize hybrids, resulting in a tissue-specific increase of active genes in F1-hybrids compared with their inbred parents.
    The Plant cell. 10/2014;
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    ABSTRACT: Yield progress in major German crops is generated mostly due to genetic improvement over the last 30 years. Comparison of trial-station with on-farm yield reveals considerable gaps that are widening over time. Yield progress of newly released varieties for 12 crops from official German trials over the period 1983 until 2012 was analysed to assess their value for cultivation and use (VCU). We paid special attention to dissect progress into a genetic and a non-genetic (agronomic) trend in order to quantify the contribution made by new varieties and by agronomic factors. In this study, we apply mixed models including regression components for genetic and agronomic trends. Ageing effects, depending on the difference of the actual testing year and the first year of testing of a particular variety, were estimated from the difference of fungicide and non-fungicide-treated trial pairs. Significant yield losses were found in all cereal crops due to assumed ageing effects. We compared national on-farm with official VCU trial yields with particular focus on whether gaps are widening over time. Results indicated a significant widening over time. In order to facilitate comparisons of results across crops, we calculated percent rates based on 1983 yield levels obtained from regression estimates. Most of the yield progress was generated by genetic improvement, and was linear showing no levelling-off. Ageing and selection effects need to be taken into account, because they may lead to overestimation of genetic trends. This study showed that contribution of agronomic factors is of minor importance in overall yield progress.
    TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik. 10/2014;
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    ABSTRACT: Outliers often pose problems in analyses of data in plant breeding but their influence on the performance of methods for estimating predictive accuracy in genomic prediction studies has not yet been evaluated. Here, we evaluate the influence of outliers on the performance of methods for accuracy estimation in genomic prediction studies using simulation. We simulated 1000 datasets for each of 10 scenarios to evaluate the influence of outliers on the performance of seven methods for estimating accuracy. These scenarios are defined by the number of genotypes, marker effect variance and magnitude of outliers. To mimic outliers, we added to one observation in each simulated data set, in turn, 5, 8 and 10 times the error standard deviation used to simulate small and large phenotypic datasets. The effect of outliers on accuracy estimation was evaluated by comparing deviations in the estimated and true accuracies for data sets with and without outliers. Outliers adversely influenced accuracy estimation, more so at small values of genetic variance or number of genotypes. A method for estimating heritability and predictive accuracy in plant breeding and another used to estimate accuracy in animal breeding were the most accurate and resistant to outliers across all scenarios and are therefore preferable for accuracy estimation in genomic prediction studies. The performances of all the other five methods that use cross-validation were less consistent and varied widely across scenarios. The computing time for the methods increased as the size of outliers and sample size increased and the genetic variance decreased.
    G3 (Bethesda, Md.). 10/2014;
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    ABSTRACT: Climate change and variability can severely constrain the productivity of pastoral herds by reducing water availability, forage production and quality, and hence the carrying capacity of rangelands. In particular, the risk of heavy livestock losses suffered during recurrent severe droughts associated with climate change and variability presents one of the most serious threats to pastoral livestock keepers. To generate insights into how climate change and variability adversely affect cattle production in the Borana of southern Ethiopia, we analyzed perceptions of herders and long-term changes in cattle numbers and climate data. A total of 242 households were surveyed to generate data on perceived trends in climate, rangeland condition and livestock production. Socio-demographic characteristics of households and cattle mortality due to the 2010/2011 drought were also recorded. Using a local time calendar, cattle herd history was reconstructed for a period spanning five major droughts to portray the linkage between changes in cattle numbers and changes in rainfall and temperature. Most of the herders perceived that rainfall has become more unpredictable, less in amount and shorter in duration, while drought recurrence and temperature have increased. Similarly, the majority perceived a decreasing trend in cattle herd sizes and their production performances. The 2010/2011 drought was associated with a substantial decline in cattle herd sizes due to increased mortality (26%) and forced off-take (19%). Death occurrences and mortality rates varied significantly by district, herd size and feed supplementation. Spectral density analysis revealed a quasi-periodic pattern in the annual rainfall with an approximate cycle period of 8.4 years, suggesting that droughts recur approximately every 8.4 years. A downward trend in cattle population mirrored a similar underlying trend in the interannual rainfall variation. Accordingly, changes in cattle number were significantly linked with changes in rainfall. In conclusion, perceptions corroborated by empirical evidences showed that climate change and variability were associated with declining cattle numbers, portending a precarious future to the sustainability of cattle pastoralism in southern Ethiopia and other pastoral systems.
    Agricultural Systems 09/2014; · 2.50 Impact Factor
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    ABSTRACT: Widespread and more frequently occurring drought conditions are a consequence of global warming and increase the demand for tolerant crop varieties to feed the growing world population. A better understanding of the molecular mechanisms underlying the water deficit response of crops will enable targeted breeding strategies to develop robust cultivars.
    BMC genomics. 08/2014; 15(1):741.
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    ABSTRACT: Abstract Text: A total number of 86’899 hatching eggs were collected from 4 different lines consisting of white- and brown laying stock from selected commercial populations and experimental lines. Heritability and breeding values were estimated for early, medium, and late embryonic survival ability and hatchability. The estimated heritabilities were low and ranged from 0.029 to 0.188 for different traits. Based on estimated breeding values, 2’082 yolk sample from 732 high and low hatching commercial hens and 495 yolk samples from 171 hens from experimental lines were sampled to determine metabolite profiles using gas chromatography–mass spectrometry. A total number of 109 different metabolites known in egg yolk, including fatty acids, amino acids carbohydrates, steroids, glycerides, vitamins, and organic acids, were detected. Using association analysis, metabolites of different components were identified which have a significant influence on embryonic survival ability. Keywords: Laying Hens Metabolites Hatchability
    10th World Congress on Genetics Applied to Livestock Production; 08/2014
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    ABSTRACT: Genomic prediction is becoming a daily tool for plant breeders. It makes use of genotypic information to make predictions used for selection decisions. The accuracy of the predictions depends on the number of genotypes used in the calibration; hence, there is a need of combining data across years. A proper phenotypic analysis is a crucial prerequisite for accurate calibration of genomic prediction procedures. We compared stage-wise approaches to analyse a real dataset of a multi-environment trial (MET) in rye, which was connected between years only through one check, and used different spatial models to obtain better estimates, and thus, improved predictive abilities for genomic prediction. The aims of this study were to assess the advantage of using spatial models for the predictive abilities of genomic prediction, to identify suitable procedures to analyse a MET weakly connected across years using different stage-wise approaches, and to explore genomic prediction as a too l for selection of models for phenotypic data analysis.
    BMC genomics. 08/2014; 15(1):646.
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    ABSTRACT: Analyses of registration trials of winter barley suggested that yield and yield stability can be enhanced by developing hybrid instead of line varieties. Yield stability is central to cope with the expected increased frequency of extreme weather conditions. The objectives of our study were to (1) examine the dimensioning of field trials needed to precisely portray yield stability of individual winter barley (Hordeum vulgare L.) genotypes, (2) compare grain yield performance and yield stability of two-rowed lines with those of six-rowed lines and hybrids, and (3) investigate the association of various agronomic traits with yield stability. Static and dynamic yield stability as well as grain yield performance was determined in five series of 3-year registration trials of winter barley in Germany. Each series included 4 or 5 six-rowed hybrids, 40-46 six-rowed inbred lines, as well as 42-49 two-rowed inbred lines. The genotypes were evaluated in 10-45 environments, i.e. year-by-location combinations. We found that precise assessment of yield stability of individual genotypes requires phenotyping in at least 40 test environments. Therefore, selection for yield stability is not usually feasible since the required number of test environments exceeds the common capacity of barley breeding programs. Also, indirect improvement of yield stability by means of agronomic traits seemed not possible since there was no constant association of any agronomic trait with yield stability. We found that compared with line varieties, hybrids showed on average higher grain yield performance combined with high dynamic yield stability. In conclusion, breeding hybrid instead of line varieties may be a promising way to develop high yielding and yield stable varieties.
    Theoretical and Applied Genetics 07/2014; · 3.66 Impact Factor
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    Hans-Peter Piepho
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    ABSTRACT: Network meta-analysis can be used to combine results from several randomized trials involving more than two treatments. Potential inconsistency among different types of trial (designs) differing in the set of treatments tested is a major challenge, and application of procedures for detecting and locating inconsistency in trial networks is a key step in the conduct of such analyses.
    BMC Medical Research Methodology 05/2014; 14(1):61. · 2.21 Impact Factor
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    ABSTRACT: The calibration data for genomic prediction should represent the full genetic spectrum of a breeding program. Data heterogeneity is minimized by connecting data sources through highly related test units. One of the major challenges of genome-enabled prediction in plant breeding lies in the optimum design of the population employed in model training. With highly interconnected breeding cycles staggered in time the choice of data for model training is not straightforward. We used cross-validation and independent validation to assess the performance of genome-based prediction within and across genetic groups, testers, locations, and years. The study comprised data for 1,073 and 857 doubled haploid lines evaluated as testcrosses in 2 years. Testcrosses were phenotyped for grain dry matter yield and content and genotyped with 56,110 single nucleotide polymorphism markers. Predictive abilities strongly depended on the relatedness of the doubled haploid lines from the estimation set with those on which prediction accuracy was assessed. For scenarios with strong population heterogeneity it was advantageous to perform predictions within a priori defined genetic groups until higher connectivity through related test units was achieved. Differences between group means had a strong effect on predictive abilities obtained with both cross-validation and independent validation. Predictive abilities across subsequent cycles of selection and years were only slightly reduced compared to predictive abilities obtained with cross-validation within the same year. We conclude that the optimum data set for model training in genome-enabled prediction should represent the full genetic and environmental spectrum of the respective breeding program. Data heterogeneity can be reduced by experimental designs that maximize the connectivity between data sources by common or highly related test units.
    Theoretical and Applied Genetics 04/2014; · 3.66 Impact Factor
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    Johannes Forkman, Hans-Peter Piepho
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    ABSTRACT: The genotype main effects and genotype-by-environment interaction effects (GGE) model and the additive main effects and multiplicative interaction (AMMI) model are two common models for analysis of genotype-by-environment data. These models are frequently used by agronomists, plant breeders, geneticists and statisticians for analysis of multi-environment trials. In such trials, a set of genotypes, for example, crop cultivars, are compared across a range of environments, for example, locations. The GGE and AMMI models use singular value decomposition to partition genotype-by-environment interaction into an ordered sum of multiplicative terms. This article deals with the problem of testing the significance of these multiplicative terms in order to decide how many terms to retain in the final model. We propose parametric bootstrap methods for this problem. Models with fixed main effects, fixed multiplicative terms and random normally distributed errors are considered. Two methods are derived: a full and a simple parametric bootstrap method. These are compared with the alternatives of using approximate F-tests and cross-validation. In a simulation study based on four multi-environment trials, both bootstrap methods performed well with regard to Type I error rate and power. The simple parametric bootstrap method is particularly easy to use, since it only involves repeated sampling of standard normally distributed values. This method is recommended for selecting the number of multiplicative terms in GGE and AMMI models. The proposed methods can also be used for testing components in principal component analysis.
    Biometrics 03/2014; · 1.41 Impact Factor
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    ABSTRACT: Water, forage and predation constrain ungulate distributions in savannas. To understand these constraints, we characterized distributions of 15 herbivore species from water, locations of peak density and degree of clustering around the peaks using zero-inflated count data models and mapping census data collected in the Mara reserve and the adjoining pastoral ranches in Kenya during a wet and dry year. Herbivores followed a humped pattern (n = 46), suggesting constrained foraging in which they balance the benefits of proximity to water with the costs of foraging where food is depleted near water and travelling to more abundant food distant from water; an exponentially decreasing pattern (n = 11), indicating strong attraction to water or vegetation near water; or a uniform (n = 3) pattern. The details rather than the types of these patterns varied between years. Herbivores concentrated farther from water and more tightly around locations of their peak densities in the ranches than the reserve. Herbivores were more abundant and widely distributed from water in the wet than the dry year, and segregated along the distance-to-water gradient, presumably to minimize interspecific competition for food. Pastoralism compressed herbivore distributions and partially excluded some species (warthog, hartebeest, topi, wildebeest, zebra, eland, buffalo and elephant) from, while attracting others (Grant’s and Thomson’s gazelles, impala, giraffe) to the ranches, relative to the reserve. Regulating cultivation, fencing, settlements and livestock stocking levels in the ranches would allow continued wildlife access to water, reduce competition with, displacement or harassment of wildlife by people, livestock and dogs near water.
    Biodiversity and Conservation 03/2014; 23(3). · 2.26 Impact Factor
  • Jens Moehring, Emlyn R Williams, Hans-Peter Piepho
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    ABSTRACT: The paper shows that unreplicated designs in multi-environmental trials are most efficient. If replication per environment is needed then augmented p-rep designs outperform augmented and replicated designs in triticale and maize. In plant breeding, augmented designs with unreplicated entries are frequently used for early generation testing. With limited amount of seed, this design allows to use a maximum number of environments in multi-environmental trials (METs). Check plots enable the estimation of block effects, error variances and a connection of otherwise unconnected trials in METs. Cullis et al. (J Agri Biol Environ Stat 11:381-393, 2006) propose to replace check plots from a grid-plot design by plots of replicated entries leading to partially replicated (p-rep) designs. Williams et al. (Biom J 53:19-27, 2011) apply this idea to augmented designs (augmented p-rep designs). While p-rep designs are increasingly used in METs, a comparison of the efficiency of augmented p-rep designs and augmented designs in the range between replicated and unreplicated designs in METs is lacking. We simulated genetic effects and allocated them according to these four designs to plot yields of a triticale and a maize uniformity trial. The designs varied in the number of environments, but have a fixed number of entries and total plots. The error model and the assumption of fixed or random entry effects were varied in simulations. We extended our simulation for the triticale data by including correlated entry effects which are common in genomic selection. Results show an advantage of unreplicated and augmented p-rep designs and a preference for using random entry effects, especially in case of correlated effects reflecting relationships among entries. Spatial error models had minor advantages compared to purely randomization-based models.
    Theoretical and Applied Genetics 02/2014; · 3.66 Impact Factor
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    ABSTRACT: Long-term yield trends have genetic and non-genetic components which may be dissected by a linear mixed model with regression terms. Disease-resistance breakdown must be accounted for in the interpretation. Long-term yield trends of crop varieties may be studied to identify a genetic trend component due to breeding efforts and a non-genetic trend component due to advances in agronomic practices. Many such studies have been undertaken, and most of these inspect trends either by plotting means against years and/or by some kind of regression analysis based on such plots. Dissection of genetic and non-genetic trend components is a key challenge in such analyses. In the present paper, we consider mixed models with regression components for identifying different sources of trend. We pay particular attention to the effect of disease breakdown, which is shown to be confounded with long-term genetic and non-genetic trends, causing an over-estimation of genetic trends based on long-term yield trial data. The models are illustrated using German multi-environment trial data on yield, mildew and Septoria leaf blotch susceptibility for winter wheat and yield, mildew and net blotch susceptibility for spring barley.
    Theoretical and Applied Genetics 02/2014; · 3.66 Impact Factor
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    ABSTRACT: Heterosis, the greater vigor of hybrids compared to their parents, has been exploited in maize breeding for more than 100 years to produce ever better performing elite hybrids of increased yield. Despite extensive research, the underlying mechanisms shaping the extent of heterosis are not well understood, rendering the process of selecting an optimal set of parental lines tedious. This study is based on a dataset consisting of 112 metabolite levels in young roots of four parental maize inbred lines and their corresponding twelve hybrids, along with the roots' biomass as a heterotic trait. Because the parental biomass is a poor predictor for hybrid biomass, we established a model framework to deduce the biomass of the hybrid from metabolite profiles of its parental lines. In the proposed framework, the hybrid metabolite levels are expressed relative to the parental levels by incorporating the standard concept of additivity/dominance, which we name the Combined Relative Level (CRL). Our modeling strategy includes a feature selection step on the parental levels which are demonstrated to be predictive of CRL across many hybrid metabolites. We demonstrate that these selected parental metabolites are further predictive of hybrid biomass. Our approach directly employs the diallel structure in a multivariate fashion, whereby we attempt to not only predict macroscopic phenotype (biomass), but also molecular phenotype (metabolite profiles). Therefore, our study provides the first steps for further investigations of the genetic determinants to metabolism and, ultimately, growth. Finally, our success on the small-scale experiments implies a valid strategy for large-scale experiments, where parental metabolite profiles may be used together with profiles of selected hybrids as a training set to predict biomass of all possible hybrids.
    PLoS ONE 01/2014; 9(1):e85435. · 3.53 Impact Factor
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    ABSTRACT: AUXIN/INDOLE-3-ACETIC ACID (Aux/IAA) proteins are central regulators of auxin signal transduction. They control many aspects of plant development, share a conserved domain structure and are localized in the nucleus. In the present study, five maize Aux/IAA proteins (ZmIAA2, ZmIAA11, ZmIAA15, ZmIAA20 and ZmIAA33) representing the evolutionary, phylogenetic and expression diversity of this gene family were characterized. Subcellular localization studies revealed that ZmIAA2, ZmIAA11 and ZmIAA15 are confined to the nucleus while ZmIAA20 and ZmIAA33 are localized in both the nucleus and the cytoplasm. Introduction of specific point mutations in the degron sequence (VGWPPV) of domain II by substituting the first proline by serine or the second proline by leucine stabilized the Aux/IAA proteins. While protein half-life times between ∼11 min (ZmIAA2) to ∼120 min (ZmIAA15) were observed in wild-type proteins, the mutated forms of all five proteins were almost as stable as GFP control proteins. Moreover, all five maize Aux/IAA proteins repressed downstream gene expression in luciferase assays to different degrees. In addition, bimolecular fluorescence complementation (BiFC) analyses demonstrated interaction of all five Aux/IAA proteins with RUM1 (ROOTLESS WITH UNDETECTABLE MERISTEM 1, ZmIAA10) while only ZmIAA15 and ZmIAA33 interacted with the RUM1 paralog RUL1 (RUM-LIKE 1, ZmIAA29). Moreover, ZmIAA11, ZmIAA15 ZmIAA33 displayed homotypic interaction. Hence, despite their conserved domain structure, maize Aux/IAA proteins display a significant variability in their molecular characteristics which is likely associated with the wide spectrum of their developmental functions.
    PLoS ONE 01/2014; 9(9):e107346. · 3.53 Impact Factor
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    ABSTRACT: Genomic selection has been routinely implemented in plant breeding in two stages. The first stage usually omits the marker information and estimates adjusted means of genotypes across environments. The second stage uses the adjusted means to predict genomic breeding values. However, if the effects of markers vary substantially between different environments, it may be important to account for this variation for varieties adapted to different environments. Using two maize data sets, we investigated whether modelling the marker-by-environment interaction can improve the predictive ability of genomic selection relative to modelling genotype-by-environment interaction alone. Modelling the marker-by-environment interaction did not substantially increase the predictive ability relative to modelling only the genotype-by-environment interaction for the two tested data sets. Thus, genomic selection, carried out in a stagewise fashion, such that the marker information is omitted until the last stage of the process, may suffice for most practical purposes. Moreover, predictive ability did not reduce substantially even when the number of markers with consistent effects across environments used for genomic prediction was reduced to about 50.
    Plant Breeding 12/2013; 132(6). · 1.18 Impact Factor
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    ABSTRACT: Energy crop production for fermentation in biogas plants significantly increased in the last years. At present maize is the most common energy crop for biogas production due to high dry matter and methane yields together with advanced breeding activities. In Germany nearly 80% of all energy crop biogas production is based on maize for silage. This dominance frequently causes environmental risks like soil erosion, nutrient losses and increased use of pesticides. Furthermore it puts societies’ acceptance towards biogas production increasingly at risk. Thus, the development of innovative cropping systems is essential for a sustainable energy crop production. This paper reviews results from 3 years field trials at seven locations with 12 different double-cropping systems (turnip rape, rye and mixture of rye–winterpea as first crops and maize, sorghum, sunflower and mixture of maize–sunflower as succeeding second crops) in comparison with three sole crops systems as references: maize and sunflower after mustard as catch crop and energy rye, harvested at dough ripeness as whole-crop silage. Double-cropping systems with rye or rye–pea as first crop and maize as second crop achieved highest DM yields of 23 t DM ha−1 on average, followed by sole cropped maize and double-cropping system of turnip rape and maize. Sole-cropped sunflowers had the lowest yield with nearly 15 t DM ha−1, followed by winter rye harvested at dough ripeness. Yields of other double-cropping systems had an intermediate position between those treatments. Methane yield per hectare was positively correlated with DM yield. Double cropping systems mostly achieved lower DM contents than sole cropping systems due to later sowing and altered photoperiodic influences. Further research on cultivars suitable for late sowing dates is necessary. Methane yield of crops was very similar with 282–298 Nl kg−1 oDM, except for sorghum with 255 Nl kg−1 oDM on average. Hence, differences in chemical components did not result in large changes regarding to methane yield, again except for sorghum, probably due to higher lignin contents. Double-cropping systems had mostly higher yield stability than sole cropping systems. The cultivation of two crops within 1 year may also spread the risk of weather extremes among two crops (or more if mixtures are grown) resulting in higher yield stability. This property is getting increasingly important with regard to climate change. Hence, double-cropping systems may contribute to a more sustainable energy crop production.
    European Journal of Agronomy 11/2013; 51:120–129. · 2.80 Impact Factor
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    ABSTRACT: We present experimental data for wheat, barley, and triticale suggesting that hybrids manifest on average higher yield stability than inbred lines. Yield stability is assumed to be higher for hybrids than for inbred lines, but experimental data proving this hypothesis is scarce for autogamous cereals. We used multi-location grain yield trials and compared the yield stability of hybrids versus lines for wheat (Triticum aestivum L.), barley (Hordeum vulgare L.), and triticale (×Triticosecale Wittmack). Our study comprised three phenotypic data sets of 1,749 wheat, 96 barley, and 130 triticale genotypes, which were evaluated for grain yield in up to five contrasting locations. Yield stability of the group of hybrids was compared with that of the group of inbred lines estimating the stability variance. For all three crops we observed a significantly (P < 0.05) higher yield stability of hybrids compared to lines. The enhanced yield stability of hybrids as compared to lines represents a major step forward, facilitating coping with the increasing abiotic stress expected from the predicted climate change.
    Theoretical and Applied Genetics 10/2013; · 3.66 Impact Factor

Publication Stats

1k Citations
291.28 Total Impact Points

Institutions

  • 2002–2014
    • Hohenheim University
      • • Institute of Plant Production and Agroecology in the Tropics and Subtropics
      • • Institute of Crop Science
      • • State Plant Breeding Institute
      Stuttgart, Baden-Württemberg, Germany
  • 2013
    • University of Bonn
      • Institute of Crop Science and Resource Conservation (INRES)
      Bonn, North Rhine-Westphalia, Germany
  • 2006–2010
    • University of Tuebingen
      • Center for Plant Molecular Biology
      Tübingen, Baden-Wuerttemberg, Germany
  • 2009
    • Max Planck Institute for Plant Breeding Research
      Köln, North Rhine-Westphalia, Germany
  • 1991–2009
    • Christian-Albrechts-Universität zu Kiel
      • Institute of Crop Science and Plant Breeding
      Kiel, Schleswig-Holstein, Germany
    • Università degli Studi di Torino
      Torino, Piedmont, Italy
  • 2008
    • Leibniz Institute of Plant Genetics and Crop Plant Research
      Gatersleben, Saxony-Anhalt, Germany
  • 1994–2008
    • Universität Kassel
      • Department of Grassland Science and Renewable Plant Resources
      Cassel, Hesse, Germany