Design of breeding strategies for energy maize in Central Europe

The area of maize (Zea mays L.) grown for production of biogas has tremendously increased in Germany during the past decade. Thus, breeding companies have a keen interest to develop special varieties for this new market segment. A high methane yield per area (MY), which depends multiplicatively on dry matter yield (DMY) and methane fermentation yield (MFY), is required to ensure the efficiency of biogas maize cultivation. However, information on the targeted biogas maize ideotype is still missing and estimates of relevant quantitative genetic parameters for representative material are required to design optimum breeding strategies. We conducted a large field experiment to assess the relevant traits in biogas maize, their variation, and associations among them. In detail, our objectives were to (1) determine MFY and its production kinetics as well as the chemical composition, (2) examine the relationship of MFY and traits related to its kinetics with plant chemical composition and silage quality traits like in vitro digestible organic matter (IVDOM) and metabolizable energy concentration (MEC); (3) examine the potential of near infrared spectroscopy (NIRS) for prediction of traits related to methane production; (4) evaluate a large population of inbred lines and their testcrosses under field conditions for agronomic and quality traits; (5) estimate variance components and heritabilities (h2) of traits relevant to biogas production; (6) study correlations among traits as well as between inbred line per se (LP) and testcross performance (TP); and (7) draw conclusions for breeding maize as a substrate for biogas production. For this purpose, a representative set of 285 dent inbred lines from diverse origins and their 570 testcross progenies with two adapted flint testers was produced. Both material groups were evaluated in field experiments conducted in six environments (three locations, two years) in Germany. For analysis of MFY, samples of a diverse core set of 16 inbred lines and their 32 testcrosses were analyzed using the Hohenheim Biogas Yield Test, a discontinuous, laboratory fermentation assay. The kinetics of methane production was assessed by non-linear regression. Estimates of h2 for MFY measured after short fermentation time (3 days) were high, but genotypic variance and, therefore, also h2 decreased towards the end of the fermentation period (35 days). This was presumably the consequence of a nearly complete degradation of all chemical components during the long fermentation period. This interpretation was supported by strong correlations of MFY with chemical components, IVDOM and MEC for the early, but not the late fermentation stages. Based on the samples in the core set, NIRS calibrations were developed for MFY, parameters related to the kinetics of methane production, and chemical composition. With a coefficient of determination from validation (R2V) of 0.82, accuracy of prediction was sufficiently high for the maximum methane production rate, which is related to the early fermentation phase, but not satisfactory for the time needed to reach 95% of a sample?s final MFY (R2V = 0.51). In agreement with the trend of h2, performance of NIRS to predict MFY on day 35 (R2V = 0.77) was lower than for MFY on day 3 (R2V = 0.85), but still at a satisfactory level, as was the case for concentrations of different chemical components. Hence, NIRS proved to be a powerful tool for prediction of MFY and chemical composition in the main experiment. For TP, estimates of variance components from the main experiments revealed that general combining ability (GCA) was the major source of variation. The very tight correlation of MY with DMY but not with MFY indicated that variation in MY was primarily attributable to differences in DMY. Compared to MEC, MFY showed a weaker association with chemical composition. Genotypic correlation (rg) of MFY was strongest with non-degradable lignin (-0.58). Correlation of MFY with starch was not significant and indicated a lower importance of high cob proportions for biogas maize than for forage maize. Hence, to improve MY, selection should primarily focus on increasing DMY. Results for LP in the main experiment largely confirmed results from testcrosses and favor selection for high dry matter yielding genotypes with less emphasis on ear proportion. Estimates of rg between LP and GCA were highest (> 0.94) for maturity traits (days to silking, dry matter concentration) and moderate (> 0.65) for DMY and MY. Indirect selection for GCA on basis of LP looks promising for maturity traits, plant height, and to some extent also for DMY.In den letzten Jahren hat die Anbaufläche von Mais (Zea mays L.) zur Biogasproduktion in Deutschland stark zugenommen. Für Saatzuchtfirmen lohnt es sich deshalb, dieses Marktsegment mit speziell dafür entwickelten Sorten zu bedienen. Für einen effizienten Biogasmaisanbau muss der Methanertrag pro Fläche, welcher sich aus dem Trockenmasseertrag (TME) und der Methanausbeute zusammensetzt, möglichst hoch sein. Bislang ist der anzustrebende Biogasmais-Idealtyp jedoch noch offen und Schätzwerte für diverse quantitativ-genetische Parameter aus repräsentativem Zuchtmaterial werden benötigt, um effiziente Züchtungsstrategien zu formulieren. Untersuchungsgegenstände der vorliegenden Arbeit waren: (1) die Bestimmung der Methanausbeute, deren Produktionskinetik sowie verschiedener relevanter Inhaltsstoffe; (2) die Assoziation dieser Parameter mit Inhaltsstoffen und Silomais-Qualitätsparametern wie in vitro verdauliche organische Substanz (IVDOM) und umsetzbare Energie (ME); (3) die Erforschung der Einsatzmöglichkeiten von Nah-Infrarot Spektroskopie (NIRS) zur Vorhersage der Methanausbeute und verwandter Merkmale; (4) die Evaluation von Inzuchtlinien und deren Testkreuzungsnachkommen bezüglich agronomischer Eigenschaften und Qualitätsmerkmalen; (5) die Schätzung von Varianzkomponenten und Heritabilität (h2) der für die Biogasproduktion relevanten Merkmale; (6) die Schätzung der Korrelationen zwischen Merkmalen sowie zwischen der Eigenleistung der Inzuchtlinien (LP) und deren Testkreuzungs-Leistung (TP); und (7) Schlussfolgerungen für die Züchtung von Biogasmais. Zu diesem Zweck wurde ein repräsentativer Satz von 285 Dent-Inzuchtlinien verschiedener Herkunft (Europa, US Corn Belt, tropisch) sowie deren 570 Testkreuzungsnachkommen mit zwei adaptierten Flint-Testern erstellt. Beide Materialgruppen wurden in Feldexperimenten in sechs Umwelten (drei Orte, zwei Jahre) in Deutschland evaluiert. Für die Untersuchung der Methanausbeute wurde ein Kernsatz von 16 Inzuchtlinien und deren 32 Testkreuzungsnachkommen mit dem Hohenheimer Biogasertragstest, einem diskontinuierlichen Fermentationsversuch, analysiert. Nicht-lineare Regressionsmodelle wurden verwendet, um die Methan-Produktionskinetik zu beschreiben. Für die Methanausbeute nach kurzer Fermentationszeit (bis 5 Tage) wurden hohe h2-Werte erzielt. Die genotypische Varianz, und somit auch h2, nahm jedoch mit fortschreitender Fermentationszeit ab. Dies ist vermutlich darauf zurückzuführen, dass die meisten Inhaltsstoffe größtenteils abgebaut und somit Unterschiede zwischen Genotypen nivelliert wurden. Diese Interpretation wird bestärkt durch enge Korrelationen zwischen Methanausbeute und diversen Inhaltsstoffen sowie IVDOM und ME nach kurzer, jedoch nicht nach längerer Fermentationszeit. Basierend auf den Proben des Kernsatzes wurden NIRS Kalibrationen für verschiedene Methanmerkmale und Inhaltsstoffe erstellt. Wie für h2 beschrieben, nahm die Güte der Kalibration für Methanausbeute mit zunehmender Fermentationszeit ab (R2V = 0.85 nach 3 und 0.77 nach 35 Tagen), war aber, wie auch für alle Inhaltsstoffe, auf einem akzeptablen Niveau. NIRS kann deshalb zur Bestimmung dieser Merkmale empfohlen werden. Die Analyse der Testkreuzungsnachkommen im Hauptexperiment zeigte, dass die Allgemeine Kombinationsfähigkeit (GCA) die wichtigste Variationsursache war. Da der Methanertrag eine sehr enge Korrelation mit TME, nicht jedoch mit der Methanausbeute zeigte, wird er hauptsächlich durch den TME bestimmt. Die Methanausbeute zeigte eine geringere Abhängigkeit von den Inhaltsstoffen als der ME-Gehalt. Die Methanausbeute korrelierte dabei am stärksten mit dem Gehalt an nicht abbaubarem Lignin (rg = -0.58), jedoch nicht mit dem Stärkegehalt. Dies widerspiegelt die geringere Bedeutung eines hohen Kolbenanteils für Biogasmais im Vergleich zu Silomais. Zur Steigerung des Methanertrags sollte folglich verstärkt auf einen hohen TME selektiert werden. Die Analyse der LP im Hauptexperiment bestätigte im Wesentlichen die TP-Ergebnisse und favorisiert somit auch eine Selektion auf einen hohen TME. Die genotypischen Korrelationen zwischen LP und GCA waren am stärksten (> 0.94) bei den Maturitätsmerkmalen (Blühzeitpunkt, Trockensubstanzgehalt) und lagen in einem mittleren Bereich (> 0.65) bei Trockenmasse- und Methanertrag. Indirekte Selektion für GCA auf Basis der LP während der Mehrstufenselektion scheint erfolgsversprechend für Maturitätsmerkmale, Pflanzenhöhe und, zu einem gewissen Grade, auch für TME

Breeding Alfalfa (Medicago sativa L.) in Mixture with Grasses

Cultivation of forage mixtures offers several advantages over monocultures, but forage legumes like alfalfa (Medicago sativa L.) are mostly bred in pure stands. Our goal was to assess the extent of accession-by-cultivation system interaction when alfalfa plants are grown in pure stands or in an easily adaptable nursery system together with their companion grasses, thereby determining the system most suitable for selection in mixture. Spaced plants of 50 alfalfa accessions were grown on bare soil as control treatment (CONV), in a sown sward of short growing lawn cultivars of tall fescue (Festuca arundinacea Schreb.) and red fescue (F. rubra L.) (LAWN), and in a sown sward of taller forage cultivars of the same species (FORA). Accession-by-cultivation system interaction variances were large for growth habit but small for vigor. Phenotypic correlation coefficients (rp) among the cultivation systems were high for vigor, whereby LAWN was somewhat more predictive for FORA (rp, FORA−LAWN = 0.83) than CONV (rp, FORA−CONV = 0.77). Observed accession-by-genotype interactions can be used pro or contra necessity for selection in mixture. However, the LAWN cultivation system might be a good compromise for practical breeding, allowing to account for given competition effects among species and to easily assess traits in the nursery

XMeis3 Is Necessary for Mesodermal Hox Gene Expression and Function

Hox transcription factors provide positional information during patterning of the anteroposterior axis. Hox transcription factors can co-operatively bind with PBC-class co-factors, enhancing specificity and affinity for their appropriate binding sites. The nuclear localisation of these co-factors is regulated by the Meis-class of homeodomain proteins. During development of the zebrafish hindbrain, Meis3 has previously been shown to synergise with Hoxb1 in the autoregulation of Hoxb1. In Xenopus XMeis3 posteriorises the embryo upon ectopic expression. Recently, an early temporally collinear expression sequence of Hox genes was detected in Xenopus gastrula mesoderm (see intro. P3). There is evidence that this sequence sets up the embryo's later axial Hox expression pattern by time-space translation. We investigated whether XMeis3 is involved in regulation of this early mesodermal Hox gene expression. Here, we present evidence that XMeis3 is necessary for expression of Hoxd1, Hoxb4 and Hoxc6 in mesoderm during gastrulation. In addition, we show that XMeis3 function is necessary for the progression of gastrulation. Finally, we present evidence for synergy between XMeis3 and Hoxd1 in Hoxd1 autoregulation in mesoderm during gastrulation

Early vertical distribution of roots and its association with drought tolerance in tropical maize

Background and aims Selection for deep roots to improve drought tolerance of maize (Zea mays L.) requires presence of genetic variation and suitable screening methods. Methods We examined a diverse set of 33 tropical maize inbred lines that were grown in growth columns in the greenhouse up to the 2-, 4-, and 6-leaf stage and in the field in Mexico. To determine length of roots from different depths at high throughput, we tested an approach based on staining roots with methylene blue and measuring the amount of absorbed dye as proxy measure for root length. Results Staining provided no advantage over root weights that are much easier to measure and therefore preferable. We found significant genotypic variation for all traits at the 6-leaf stage. For development rates between the 2-leaf and the 6-leaf stage, genotypes only differed for rooting depth and the number of crown roots. Positive correlations of leaf area with root length and rooting depth indicated a common effect of plant vigor. However, leaf area in growth columns was negatively related to grain yield under drought (r = −0.50). Conclusion The selection for deeper roots by an increase in plant vigor likely results in a poorer performance under drought conditions. The proportion of deep roots was independent of other traits but showed a low heritability and was not correlated to field performance. An improved screening protocol is proposed to increase throughput and heritability for this trait.ISSN:0032-079XISSN:1573-503

Breeding an organic forage crop variety (Liveseed Practice Abstract)

Practical Recommendations - Start an organic breeding program in your target selection environment, collect information about organic certified areas including their “cultivation history” (e.g. weed infestation from previous years) - Mimic future cultivation system in the nursery, e.g. by combining spaced plants of target species by under-sowing with the right companion species3 - Do preventive measures for avoidance against pests and weeds in advance, e.g. reduce seed stock of weeds via repeated hoeing - Identify important traits (e.g. early vigour to enhance weed suppression) for organic cultivation and put special focus on them in your selection

Perspectives for reducing seed shattering in ryegrasses

In the last decades, the progress in ryegrass (Loliumspp.) breeding was mainly onagronomic traits such as biomass yield, forage quality or disease resistance. However,for commercial success, a stable and high seed yield is a prerequisite for any cultivar.The realized seed yield is influenced by many different factors such as non-optimalpollination and fertilization, seed abortion and seed shattering. While seed shatteringhas been largely eliminated in major cereal crops such as rice, barley or sorghum dur-ing domestication, the trait has been largely neglected in ryegrass breeding programs.The close syntenic relationship of cereal and ryegrass genomes offers the opportu-nity to develop breeding approaches for reducing seed shattering in the latter bytransferring knowledge from the former. The objectives of this review are to (1) givean overview on the knowledge of morphology on seed shattering in cereal crops andryegrasses, (2) compare the genetic background underlying seed shattering in differ-ent species, (3) identify putative candidate genes controlling seed shattering in rye-grasses through comparative genomic analysis and (4) give an outlook on newbreeding strategies resulting in low seed shattering cultivars of ryegrasses and relatedforage grass species.ISSN:0142-5242ISSN:1365-249

A Decade of Variety Testing for Resistance of Red Clover to Southern Anthracnose (<em>Colletotrichum trifolii</em> Bain et Essary) at the Bavarian State Research Center for Agriculture (LfL)

Southern anthracnose is caused by the fungal pathogen Colletotrichum trifolii Bain et Essary and affects red clover (Trifolium pratense) cultivation, causing severe losses in plant stands. Artificial inoculation with the pathogen in the greenhouse has been proven to effectively differentiate varieties for their resistance based on the survival rates of plants. Additionally, this method was successfully used to improve red clover populations via recurrent selection. However, not much is yet known on its association with resistance behavior in the field. In this study, results from 10 years of artificial inoculation trials at the Bavarian State Research Center for Agriculture were analyzed and compared to official German variety descriptions that are based on field data. A good congruency between survival rates from the greenhouse and official susceptibility ratings were observed. Thus, data from greenhouse tests have great potential to complement official variety lists where gaps exist. It was shown that within only three generations of recurrent selection using the greenhouse test, an existing variety could be significantly improved in terms of its resistance to Southern anthracnose without changing its DUS characteristics. A continuously increasing resistance level in the varieties registered in Germany since 2005 indicates that breeders can successfully respond to the threat imposed by this relatively new disease

An image analysis pipeline for automated classification of imaging light conditions and for quantification of wheat canopy cover time series in field phenotyping

Background Robust segmentation of canopy cover (CC) from large amounts of images taken under different illumination/light conditions in the field is essential for high throughput field phenotyping (HTFP). We attempted to address this challenge by evaluating different vegetation indices and segmentation methods for analyzing images taken at varying illuminations throughout the early growth phase of wheat in the field. 40,000 images taken on 350 wheat genotypes in two consecutive years were assessed for this purpose. Results We proposed an image analysis pipeline that allowed for image segmentation using automated thresholding and machine learning based classification methods and for global quality control of the resulting CC time series. This pipeline enabled accurate classification of imaging light conditions into two illumination scenarios, i.e. high light-contrast (HLC) and low light-contrast (LLC), in a series of continuously collected images by employing a support vector machine (SVM) model. Accordingly, the scenario-specific pixel-based classification models employing decision tree and SVM algorithms were able to outperform the automated thresholding methods, as well as improved the segmentation accuracy compared to general models that did not discriminate illumination differences. Conclusions The three-band vegetation difference index (NDI3) was enhanced for segmentation by incorporating the HSV-V and the CIE Lab-a color components, i.e. the product images NDI3*V and NDI3*a. Field illumination scenarios can be successfully identified by the proposed image analysis pipeline, and the illumination-specific image segmentation can improve the quantification of CC development. The integrated image analysis pipeline proposed in this study provides great potential for automatically delivering robust data in HTFP.ISSN:1746-481