Effects of supplementing corn silage with different nitrogen sources on ruminal fermentation and microbial populations in vitro.

Compared to grass silage (GS)-, corn silage (CS)-based diets appear to increase the efficiency of microbial protein synthesis (EMPS) in the rumen. Opposite results for the EMPS obtained in vitro have raised the question of whether an inadequate supply of amino N for microbes might explain the low EMPS. We examined the effects of supplementation with different N sources in CS on the EMPS and microbial populations in vitro. GS and CS were used as substrates for in vitro incubation. CS was non-supplemented or supplemented with urea, mixed amino acids (AA), peptone, or protein to adjust the N content to that of GS. Degradation of organic matter (OM) and crude protein (CP) revealed a positive effect of all N supplements, except protein. Additionally, N supplementation increased fiber degradation of CS. Peptone primarily stimulated hemicellulolytic activity and urea stimulated cellulolytic activity. The EMPS of CS was improved by all N supplements, with peptone and urea exhibiting the highest increase (57% and 54%, respectively), followed by AA mix (40%) and protein (11%) compared to that of CS alone (111 g microbial CP kg-1 fermented OM). However, the level of EMPS detected with GS (200 g microbial CP kg-1 fermented OM) was not achieved. Protozoal 18S rRNA gene copy numbers were negatively correlated with the EMPS, whereas no correlation was found between total bacteria and the EMPS. A stimulating effect of urea, AA mix, and peptone was detected for Ruminococcus albus and Prevotella bryantii, whereas Fibrobacter succinogenes was inhibited by N supplementation. This indicated that neither the amount of available N nor the N source was the only limiting factor in the low EMPS values of CS in vitro. Information is also provided on the stimulating effects of different N sources on several microbial species in mixed rumen culture.

Effect of nitrogen fertilisation on the amino acid digestibility of different triticale genotypes in caecectomised laying hens.

BACKGROUND: The influence of nitrogen fertilisation and genotype on the amino acid (AA) digestibility of triticale grain was investigated in caecectomised laying hens. Three genotypes, Grenado, EAW6002 and Lasko, were cultivated with and without nitrogen fertilisation at the end of the heading stage. The six triticale variants as well as a basal diet were each used to feed seven laying hens in a 7 × 7 Latin square design. RESULTS: Nitrogen fertilisation influenced the digestibility of Cys, Glu, Phe and Ser in some triticale genotypes and reduced Ala, Ile, Lys, Met and Val digestibility in all genotypes (P < 0.05). Nitrogen fertilisation increased the concentration of all AAs in the grain. Consequently, the concentration of digestible AAs in the grains was increased for most AAs upon nitrogen fertilisation. Overall, Lys had the lowest digestibility, whereas that of Glu and Pro was the highest. For the triticale genotypes, the level of AA digestibility was highest for EAW6002 followed by Lasko and Grenado, with significant differences (P < 0.05) between genotypes for some but not all AAs. CONCLUSION: The results indicated that the accuracy of the digestible AA supply for hen feeding might benefit from considering fertilisation and genotype-specific digestibility data in feed formulation. © 2016 Society of Chemical Industry.

Effects of corn silage and grass silage in ruminant rations on diurnal changes of microbial populations in the rumen of dairy cows.

Here, we examined diurnal changes in the ruminal microbial community and fermentation characteristics of dairy cows fed total mixed rations containing either corn silage (CS) or grass silage (GS) as forage. The rations, which consisted of 52% concentrate and 48% GS or CS, were offered for ad libitum intake over 20 days to three ruminal-fistulated lactating Jersey cows during three consecutive feeding periods. Feed intake, ruminal pH, concentrations of short chain fatty acids and ammonia in rumen liquid, as well as abundance change in the microbial populations in liquid and solid fractions, were monitored in 4-h intervals on days 18 and 20. The abundance of total bacteria and Fibrobacter succinogenes increased in solids in cows fed CS instead of GS, and that of protozoa increased in both solid and liquid fractions. Feeding GS favored numbers of F. succinogenes and Selenomonas ruminantium in the liquid fraction as well as the numbers of Ruminobacter amylophilus, Prevotella bryantii and ruminococci in both fractions. Minor effects of silage were detected on populations of methanogens. Despite quantitative changes in the composition of the microbial community, fermentation characteristics were less affected by forage source. These results suggest a functional adaptability of the ruminal microbiota to total mixed rations containing either GS or CS as the source of forage. Diurnal changes in microbial populations were primarily affected by feed intake and differed between species and fractions, with fewer temporal fluctuations evident in the solid than in the liquid fraction. Interactions between forage source and sampling time were of minor importance to most of the microbial species examined. Thus, diurnal changes of microbial populations and fermentative activity were less affected by the two silages.

Changes in Rumen Microbial Community Composition during Adaption to an In Vitro System and the Impact of Different Forages.

This study examined ruminal microbial community composition alterations during initial adaption to and following incubation in a rumen simulation system (Rusitec) using grass or corn silage as substrates. Samples were collected from fermenter liquids at 0, 2, 4, 12, 24, and 48 h and from feed residues at 0, 24, and 48 h after initiation of incubation (period 1) and on day 13 (period 2). Microbial DNA was extracted and real-time qPCR was used to quantify differences in the abundance of protozoa, methanogens, total bacteria, Fibrobacter succinogenes, Ruminococcus albus, Ruminobacter amylophilus, Prevotella bryantii, Selenomonas ruminantium, and Clostridium aminophilum. We found that forage source and sampling time significantly influenced the ruminal microbial community. The gene copy numbers of most microbial species (except C. aminophilum) decreased in period 1; however, adaption continued through period 2 for several species. The addition of fresh substrate in period 2 led to increasing copy numbers of all microbial species during the first 2-4 h in the fermenter liquid except protozoa, which showed a postprandial decrease. Corn silage enhanced the growth of R. amylophilus and F. succinogenes, and grass silage enhanced R. albus, P. bryantii, and C. aminophilum. No effect of forage source was detected on total bacteria, protozoa, S. ruminantium, or methanogens or on total gas production, although grass silage enhanced methane production. This study showed that the Rusitec provides a stable system after an adaption phase that should last longer than 48 h, and that the forage source influenced several microbial species.

Inter-block information: to recover or not to recover it?

KEY MESSAGE: Comparing standard errors of treatment differences using fixed or random block effects with the approximation of Kackar and Harville helps in choosing the preferable assumption for blocks in the analysis of field experiments. Blocked designs are common in plant breeding field trials. Depending on the precision of variance estimates, recovery of inter-block information via random block effects may be worthwhile. A challenge in practice is to decide when recovery of information should be pursued. To investigate this question, a series of sugar beet trials laid out as α-designs were analysed assuming fixed or random block effects. Additionally, small trials laid out as α-designs or partially replicated designs were simulated and analysed assuming fixed or random block effects. Nine decision rules, including the Kackar-Harville adjustment, were used for choosing the better assumption regarding the block effects. In general, use of the Kackar-Harville adjustment works well and is recommended for partially replicated designs. For α-designs, using inter-block information is preferable for designs with four or more blocks.

The importance of phenotypic data analysis for genomic prediction - a case study comparing different spatial models in rye.

BACKGROUND: 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 tool for selection of models for phenotypic data analysis. RESULTS: Using complex spatial models did not significantly improve the predictive ability of genomic prediction, but using row and column effects yielded the highest predictive abilities of all models. In the case of MET poorly connected between years, analysing each year separately and fitting year as a fixed effect in the genomic prediction stage yielded the most realistic predictive abilities. Predictive abilities can also be used to select models for phenotypic data analysis. The trend of the predictive abilities was not the same as the traditionally used Akaike information criterion, but favoured in the end the same models. CONCLUSIONS: Making predictions using weakly linked datasets is of utmost interest for plant breeders. We provide an example with suggestions on how to handle such cases. Rather than relying on checks we show how to use year means across all entries for integrating data across years. It is further shown that fitting of row and column effects captures most of the heterogeneity in the field trials analysed.

Prevalence of neutralising antibodies against adenoviruses in lizards and snakes.

Adenoviruses (AdVs) are relatively common in lizards and snakes, and several genetically distinct AdVs have been isolated in cell culture. The aims of this study were to examine serological relationships among lizard and snake AdVs and to determine the frequency of AdV infections in these species. Isolates from a boa constrictor (Boa constrictor), a corn snake (Pantherophis gutattus) and a central bearded dragon (Pogona vitticeps), and two isolates from helodermatid lizards (Heloderma horridum and H. suspectum) were used in neutralisation tests for the detection of antibodies in plasma from 263 lizards from seven families (including 12 species) and from 141 snakes from four families (including 28 species) from the USA and Europe. Most lizard and snake samples had antibodies against a range of AdV isolates, indicating that AdV infection is common among these squamates. Neutralisation tests with polyclonal antibodies raised in rabbits demonstrated serological cross-reactivity between both helodermatid lizard isolates. However, squamate plasma showed different reactions to each of these lizard isolates in neutralisation tests.

Estimating economic thresholds for site-specific weed control using manual weed counts and sensor technology: an example based on three winter wheat trials.

BACKGROUND: Precision experimental design uses the natural heterogeneity of agricultural fields and combines sensor technology with linear mixed models to estimate the effect of weeds, soil properties and herbicide on yield. These estimates can be used to derive economic thresholds. Three field trials are presented using the precision experimental design in winter wheat. Weed densities were determined by manual sampling and bi-spectral cameras, yield and soil properties were mapped. RESULTS: Galium aparine, other broad-leaved weeds and Alopecurus myosuroides reduced yield by 17.5, 1.2 and 12.4 kg ha(-1) plant(-1) m(2) in one trial. The determined thresholds for site-specific weed control with independently applied herbicides were 4, 48 and 12 plants m(-2), respectively. Spring drought reduced yield effects of weeds considerably in one trial, since water became yield limiting. A negative herbicide effect on the crop was negligible, except in one trial, in which the herbicide mixture tended to reduce yield by 0.6 t ha(-1). Bi-spectral cameras for weed counting were of limited use and still need improvement. Nevertheless, large weed patches were correctly identified. CONCLUSION: The current paper presents a new approach to conducting field trials and deriving decision rules for weed control in farmers' fields.

Ultrafast branching in the excited state of coumarin and umbelliferone.

In the present work we have explored the ultrafast relaxation network of coumarin and umbelliferone (7-hydroxy-coumarin) using time-resolved femtosecond spectroscopy and quantum chemical calculations. Despite the importance of the photophysical properties of coumarin derivatives for applications in biomedicine, the low fluorescence quantum yield of coumarin itself has not been fully understood so far. On the basis of our combined experimental and theoretical results we suggest a model for the ultrafast decay after photoexcitation incorporating two parallel radiationless relaxation pathways: one within the initially excited state via ring opening and the other one by transition into a dark state along the carbonyl stretching mode. The fluorescence quantum yield is determined by the position of the branching point relative to the Franck-Condon region which is strongly influenced by interactions with the environment and the substitution pattern. This model is finally capable of giving a comprehensive account of the striking differences observed in the photophysical behavior of coumarin as opposed to umbelliferone.

Effect of feed supplements on dry season milk yield and profitability of crossbred cows in Honduras.

The contribution of dry season silage feeding on daily milk yield (MY) and dairying profitability in terms of income over feed cost (IOFC) was evaluated in dual-purpose cattle production systems in Honduras. MY records of 34 farms from two milk collection centres were collected over a 2-year period. Farms were surveyed to obtain information on the type, quantity and cost of supplemented feed, breed type and number of lactating cows in each month. Farms were classified in silage farms (SF, with a short silage supplementation period), non-silage farms (NSF) and prototype farms (PF, with an extended silage supplementation period). Data were analysed using descriptive statistics and a linear mixed model approach. PF had significantly higher MY than SF and NSF but, due to higher expenses for both concentrate and silage, similar IOFC compared to NSF. SF had similar MY but lower IOFC compared to NSF, due to higher feed expenses. The effect of silage feeding, particularly maize silage, on MY was significant and superior to that of other forage supplements. Silage supplementation contributed to the highest MY and IOFC on farms with crossbred cows of >62.5 % Bos taurus and to the second highest profitability on farms with >87.5 % Bos indicus share. It is concluded that silage can play an important role in drought-constrained areas of the tropics and can contribute to profitable dairying, irrespective of breed.

A stage-wise approach for the analysis of multi-environment trials.

Plant breeders and variety testing agencies routinely test candidate genotypes (crop varieties, lines, test hybrids) in multiple environments. Such multi-environment trials can be efficiently analysed by mixed models. A single-stage analysis models the entire observed data at the level of individual plots. This kind of analysis is usually considered as the gold standard. In practice, however, it is more convenient to use a two-stage approach, in which experiments are first analysed per environment, yielding adjusted means per genotype, which are then summarised across environments in the second stage. Stage-wise approaches suggested so far are approximate in that they cannot fully reproduce a single-stage analysis, except in very simple cases, because the variance-covariance matrix of adjusted means from individual environments needs to be approximated by a diagonal matrix. This paper proposes a fully efficient stage-wise method, which carries forward the full variance-covariance matrix of adjusted means from the individual environments to the analysis across the series of trials. Provided the variance components are known, this method can fully reproduce the results of a single-stage analysis. Computations are made efficient by a diagonalisation of the residual variance-covariance matrix, which necessitates a corresponding linear transformation of both the first-stage estimates (e.g. adjusted means and regression slopes for plot covariates) and the corresponding design matrices for fixed and random effects. We also exemplify the extension of the general approach to a three-stage analysis. The method is illustrated using two datasets, one real and the other simulated. The proposed approach has close connections with meta-analysis, where environments correspond to centres and genotypes to medical treatments. We therefore compare our theoretical results with recently published results from a meta-analysis.

Genome-wide association mapping reveals epistasis and genetic interaction networks in sugar beet.

Epistasis is defined as interactions between alleles of two or more genetic loci. Detection of epistatic interactions is the key to understand the genetic architecture and gene networks underlying complex traits. Here, we examined the extent of epistasis for seven quantitative traits with an association mapping approach in a large population of elite sugar beet lines. We found that correction for population stratification is required and that in terms of reducing the false-positive rate the mixed model approach including the kinship matrix performed best. In genome-wide scans, we detected both main effects and epistatic QTL. For physiological traits, the detected digenic and higher-order epistasis explained a considerable proportion of the genotypic variance. We illustrate that the identified epistatic interactions define comprehensive genetic networks, which may serve as starting points towards a systems-oriented approach to understand the regulation of complex traits.

Shaper-assisted full-phase characterization of UV pulses without a spectrometer.

A full-phase measurement of low-energy femtosecond UV pulses is presented. The method relies on phase retrieval of measured sonogram traces and is greatly simplified by a two-dimensional shaper-assisted cross correlation setup. As all required pulses are generated by the pulse shaper, the method is free of external references and additional tunable filter setups.

Genetic basis of agronomically important traits in sugar beet (Beta vulgaris L.) investigated with joint linkage association mapping.

Epistatic interactions may contribute substantially to the hybrid performance of sugar beet. The main goal of our study was to dissect the genetic basis of eight important physiological and agronomic traits using two different biometrical models for joint linkage association mapping. A total of 197 genotypes of an elite breeding population were evaluated in multi-location trials and fingerprinted with 194 SNP markers. Two different statistical models were used for the genome-wide scan for marker-trait associations: Model A, which corrects for the genetic background with markers as cofactors and Model B, which additionally models a population effect. Based on the extent of linkage disequilibrium in the parental population, we estimated that for a genome-wide scan at least 100 equally spaced markers are necessary. We mapped across the eight traits 39 QTL for Model A and 22 for Model B. Only 11% of the total number of QTL were identified based on Models A and B, which indicates that both models are complementary. Epistasis was detected only for two out of the eight traits, and contributed only to a minor extent to the genotypic variance. This low relevance of epistasis implies that in sugar beet breeding the prediction of performance of three-way hybrids is feasible with high accuracy based on the means of their single crosses.

Shaper-assisted ultraviolet cross correlator.

We successfully demonstrate the characterization of phase- and amplitude-modulated broadband UV pulses with a shaper-assisted cross-correlation setup. A two-dimensional pulse modulator, operated in the diffractive shaping mode, is used to generate an inherently temporally overlapped reference beam. To greatly improve the usability of this method, we combined this setup with a split-mirror UV autocorrelator based on a solar-blind photomultiplier tube as sensitive nonlinearity. This allows sensitive characterization of the Fourier-limited pulses down to a few picojoules, as well as complex-shaped ultrashort UV pulses, typically occurring in coherent control experiments.

Prediction of hybrid performance in maize using molecular markers and joint analyses of hybrids and parental inbreds.

The identification of superior hybrids is important for the success of a hybrid breeding program. However, field evaluation of all possible crosses among inbred lines requires extremely large resources. Therefore, efforts have been made to predict hybrid performance (HP) by using field data of related genotypes and molecular markers. In the present study, the main objective was to assess the usefulness of pedigree information in combination with the covariance between general combining ability (GCA) and per se performance of parental lines for HP prediction. In addition, we compared the prediction efficiency of AFLP and SSR marker data, estimated marker effects separately for reciprocal allelic configurations (among heterotic groups) of heterozygous marker loci in hybrids, and imputed missing AFLP marker data for marker-based HP prediction. Unbalanced field data of 400 maize dent x flint hybrids from 9 factorials and of 79 inbred parents were subjected to joint analyses with mixed linear models. The inbreds were genotyped with 910 AFLP and 256 SSR markers. Efficiency of prediction (R (2)) was estimated by cross-validation for hybrids having no or one parent evaluated in testcrosses. Best linear unbiased prediction of GCA and specific combining ability resulted in the highest efficiencies for HP prediction for both traits (R (2) = 0.6-0.9), if pedigree and line per se data were used. However, without such data, HP for grain yield was more efficiently predicted using molecular markers. The additional modifications of the marker-based approaches had no clear effect. Our study showed the high potential of joint analyses of hybrids and parental inbred lines for the prediction of performance of untested hybrids.

Multidimensional spectroscopy of beta-carotene: vibrational cooling in the excited state.

Pump-degenerate four wave mixing (Pump-DFWM) is used for investigating the vibrational dynamics in the excited state of beta-carotene in solution. In this 2D technique, an initial pump pulse promotes the system to the excited state, which is then probed by the succeeding DFWM sequence. We focus particularly on the internal conversion between the S(2) and S(1) state with high temporal and spectral resolution. The frequency shift of the excited state vibrations is measured and is explained as mode-specific vibrational cooling. Our results suggest an internal conversion in a time range between 260 and 500 fs without any intermediate states.

Molecular marker-based prediction of hybrid performance in maize using unbalanced data from multiple experiments with factorial crosses.

In hybrid breeding, the prediction of hybrid performance (HP) is extremely important as it is difficult to evaluate inbred lines in numerous cross combinations. Recent developments such as doubled haploid production and molecular marker technologies have enhanced the prospects of marker-based HP prediction to accelerate the breeding process. Our objectives were to (1) predict HP using a combined analysis of hybrids and parental lines from a breeding program, (2) evaluate the use of molecular markers in addition to phenotypic and pedigree data, (3) evaluate the combination of line per se data with marker-based estimates, (4) study the effect of the number of tested parents, and (5) assess the advantage of haplotype blocks. An unbalanced dataset of 400 hybrids from 9 factorial crosses tested in different experiments and data of 79 inbred parents were subjected to combined analyses with a mixed linear model. Marker data of the inbreds were obtained with 20 AFLP primer-enzyme combinations. Cross-validation was used to assess the performance prediction of hybrids of which no or only one parental line was testcross evaluated. For HP prediction, the highest proportion of explained variance (R (2)), 46% for grain yield (GY) and 70% for grain dry matter content (GDMC), was obtained from line per se best linear unbiased prediction (BLUP) estimates plus marker effects associated with mid-parent heterosis (TEAM-LM). Our study demonstrated that HP was efficiently predicted using molecular markers even for GY when testcross data of both parents are not available. This can help in improving greatly the efficiency of commercial hybrid breeding programs.

Association mapping in multiple segregating populations of sugar beet (Beta vulgaris L.).

Association mapping in multiple segregating populations (AMMSP) combines high power to detect QTL in genome-wide approaches of linkage mapping with high mapping resolution of association mapping. The main objectives of this study were to (1) examine the applicability of AMMSP in a plant breeding context based on segregating populations of various size of sugar beet (Beta vulgaris L.), (2) compare different biometric approaches for AMMSP, and (3) detect markers with significant main effect across locations for nine traits in sugar beet. We used 768 F(n) (n = 2, 3, 4) sugar beet genotypes which were randomly derived from 19 crosses among diploid elite sugar beet clones. For all nine traits, the genotypic and genotype x location interaction variances were highly significant (P < 0.01). Using a one-step AMMSP approach, the total number of significant (P < 0.05) marker-phenotype associations was 44. The identification of genome regions associated with the traits under consideration indicated that not only segregating populations derived from crosses of parental genotypes in a systematic manner could be used for AMMSP but also populations routinely derived in plant breeding programs from multiple, related crosses. Furthermore, our results suggest that data sets, whose size does not permit analysis by the one-step AMMSP approach, might be analyzed using the two-step approach based on adjusted entry means for each location without losing too much power for detection of marker-phenotype associations.

Comparison of mixed-model approaches for association mapping.

Association-mapping methods promise to overcome the limitations of linkage-mapping methods. The main objectives of this study were to (i) evaluate various methods for association mapping in the autogamous species wheat using an empirical data set, (ii) determine a marker-based kinship matrix using a restricted maximum-likelihood (REML) estimate of the probability of two alleles at the same locus being identical in state but not identical by descent, and (iii) compare the results of association-mapping approaches based on adjusted entry means (two-step approaches) with the results of approaches in which the phenotypic data analysis and the association analysis were performed in one step (one-step approaches). On the basis of the phenotypic and genotypic data of 303 soft winter wheat (Triticum aestivum L.) inbreds, various association-mapping methods were evaluated. Spearman's rank correlation between P-values calculated on the basis of one- and two-stage association-mapping methods ranged from 0.63 to 0.93. The mixed-model association-mapping approaches using a kinship matrix estimated by REML are more appropriate for association mapping than the recently proposed QK method with respect to (i) the adherence to the nominal alpha-level and (ii) the adjusted power for detection of quantitative trait loci. Furthermore, we showed that our data set could be analyzed by using two-step approaches of the proposed association-mapping method without substantially increasing the empirical type I error rate in comparison to the corresponding one-step approaches.