Breeding maize as biogas substrate in Central Europe: II. Quantitative-genetic parameters for inbred lines and correlations with testcross performance

Breeding maize for use as a biogas substrate (biogas maize) has recently gained considerable importance. To optimize hybrid breeding programs, information about line per se performance (LP) of inbreds and its relation to their general combining ability (GCA) is required. The objectives of our research were to (1) estimate variance components and heritability of LP for agronomic and quality traits relevant to biogas production, (2) study correlations among traits as well as between LP and GCA, and (3) discuss implications for breeding of biogas maize. We evaluated 285 diverse dent maize inbred lines in six environments. Data were recorded on agronomic and quality traits, including dry matter yield (DMY), methane fermentation yield (MFY), and their product, methane yield (MY), as the main target trait. In agreement with observations made for GCA in a companion study, variation in MY was mainly determined by DMY. MFY, which showed moderate correlation with lignin but only weak correlation with starch, revealed only low genotypic variation. Thus, our results favor selection of genotypes with high DMY and less focus on ear proportion for biogas maize. Genotypic correlations between LP and GCA [r _g (LP, GCA)] were highest (≥0.94) for maturity traits (days to silking, dry matter concentration) and moderate (≥0.65) for DMY and MY. Multistage selection is recommended. Selection for GCA of maturity traits, plant height, and to some extent also quality traits and DMY on the level of LP looks promising.     

用TOPSIS综合评价小麦种质资源对麦长管蚜的抗虫性

以107份小麦（Triticum aestivum L.）种质资源为材料,分析了各基因型的抗麦长管蚜特征,共发现1份高抗材料和19份中抗材料。另外,对各供试材料的分蘖数、千粒重、抽穗期等10个小麦农艺性状指标进行了调查。经相关性检验发现,分蘖数和株高均与抗虫指数和抗虫级别呈极显著负相关。综合抗虫指数、抗虫级别和10个小麦农艺性状共12个特征数据进行TOPSIS分析,结果发现,来自伊朗的小麦品种PI 623081表现最好,其次是华麦8号、黔麦15和PI 634770等,新麦19的表现最差。研究结果对小麦的抗虫育种工作具有极其重要的指导意义。     

Determining the Most Important Physiological and Agronomic Traits Contributing to Maize Grain Yield through Machine Learning Algorithms: A New Avenue in Intelligent Agriculture

Prediction is an attempt to accurately forecast the outcome of a specific situation while using input information obtained from a set of variables that potentially describe the situation. They can be used to project physiological and agronomic processes; regarding this fact, agronomic traits such as yield can be affected by a large number of variables. In this study, we analyzed a large number of physiological and agronomic traits by screening, clustering, and decision tree models to select the most relevant factors for the prospect of accurately increasing maize grain yield. Decision tree models (with nearly the same performance evaluation) were the most useful tools in understanding the underlying relationships in physiological and agronomic features for selecting the most important and relevant traits (sowing date-location, kernel number per ear, maximum water content, kernel weight, and season duration) corresponding to the maize grain yield. In particular, decision tree generated by C&RT algorithm was the best model for yield prediction based on physiological and agronomical traits which can be extensively employed in future breeding programs. No significant differences in the decision tree models were found when feature selection filtering on data were used, but positive feature selection effect observed in clustering models. Finally, the results showed that the proposed model techniques are useful tools for crop physiologists to search through large datasets seeking patterns for the physiological and agronomic factors, and may assist the selection of the most important traits for the individual site and field. In particular, decision tree models are method of choice with the capability of illustrating different pathways of yield increase in breeding programs, governed by their hierarchy structure of feature ranking as well as pattern discovery via various combinations of features.     

Sensitivity of different flax (Linum usitatissimum L.) genotypes to salinity determined by GE biplot

Plants respond differently to salt stress depending on their genetic structure and the severity of the stress. Salinity reduces seed germination, delays plant emergence, and inhibits seedling growth. The selection of the tolerant genotypes, however, plays a vital role in increasing agricultural output since various genotypes greatly vary for their tolerance to salinity. Therefore, this study determined the impact of five different NaCl levels (i.e., 0, 50, 100, 150 and 200 mM) on seed germination and growth attributes of 10 flax (Linum usitatissimum L.) genotypes. The germination and growth characteristics of the genotypes under study were examined using the biplot approach at varied salt levels. The results indicated that individual and interactive effects of genotypes and salinity levels significantly (p ≤ 0.01 or p ≤ 0.05) affected several seed germination traits. The relations of genotype × germination traits indicated that ‘G4′ and ‘G6′ were the most stable genotypes with the highest performance regarding seed germination characteristics. The genotype ‘G2′ was associated with shoot length, while ‘G7′ was linked with salinity tolerance index. The biplot divided the germination characteristics into five different groups according to sector analysis. Most of the germination parameters had higher values under 100 mM, while some of the parameters had better values under 0, 50 and 200 mM NaCl levels. The tested genotypes varied for their seed germination and growth response depending on the NaCl levels. The genotypes ‘G4′, ‘G5′ and ‘G6′ proved more tolerant to high NaCl levels. Therefore, these genotypes can be used to improve flax productivity under saline soils.     

Mega-environment investigation based on the GGE biplot and genotype selection for high essential oil yield in vetiver grass (Chrysopogon zizanioides L. Roberty)

The genotype and genotype-environment interaction (GEI) significantly differed in the current study, demonstrating that genotype-environment (G × E) interaction heavily influenced genotype yield output. A combined analysis of variance revealed that the genotype and G × E interaction had a significant difference for all eleven characters. Except for the traits Ch1, Ch3, Ch4, Ch5, Ch9, and Ch11, a significant variance was found for all eleven characters over the years, indicating that the environment significantly influenced traits performance. The quality of vetiver essential oil is also affected by genotypic and environmental factors. The GGE biplot model shows that genotypes have a significant G × E interaction, split between the first and second IPCA/interaction principal component axis. In all three contexts, the genotypes V1, V13, and V14 in the center of the biplot graph were determined to be stable. The traits CH1-vs-CH3, CH1-vs-Ch7, and Ch4-vs-CH9 indicated significantly substantial genetic correlations among economic traits over the years. The traits Ch3-vs-Ch4, Ch3-vs-Ch9, Ch4-vs-Ch9, and Ch5-vs-Ch6 were highly significant and positively correlated, but Ch1-vs-Ch11 and CH7-vs-CH9 were negatively related. E1 and E2 were linked together to form one group, and E3 and E4 were linked together. GGE biplot analysis for environment interrelationships V1 and V7 was highly stable and well-performing for essential oil yield from a polygon perspective. Genotypes V1, V7, V10, V11, and V12 are stable and desirable genotypes arrayed in a concentric circle near the center of an ideal genotype. The genotypes V 17, V 41, V 69, and V 70 showed good photosynthetic efficiency. These genotypes might be suitable for large-scale farming.     

鹰嘴豆种质资源农艺性状遗传多样性分析

以100份鹰嘴豆种质资源为材料,应用聚类分析和主成分分析方法,对15个主要农艺性状的遗传多样性进行分析。结果表明,参试材料存在广泛的遗传多样性。其中,多样性指数最高的是株高,其次是百粒重;性状变异系数最大的是单株荚数,其次是单株粒重;基于各种质间形态标记的遗传差异,将100份鹰嘴豆种质聚类并划分为4大类群。第Ⅰ类群可作为选育丰产中粒型和株高适中的品种,第Ⅱ类群可作为选育矮秆耐密及特异粒色(型)品种,第Ⅲ类群丰产性较差可作为选育子粒球型、光滑的品种,第Ⅳ类群可作为选育大粒型、适宜机械化收获的品种。9个数量性状的主成分分析结果表明,前4个主成分累计贡献率达73.91%,各主成分性状载荷值反映了主要数量性状的育种选择潜力。综合分析种质资源农艺性状,为鹰嘴豆的有效利用提供一定的科学依据。     

Identification of kernel iron- and zinc-rich maize inbreds and analysis of genetic diversity using microsatellite markers

Development of micronutrient enriched staple foods is an important breeding goal in view of the extensive problem of ‘hidden hunger’ caused by micronutrient malnutrition. In the present study, kernel iron (Fe) and zinc (Zn) concentrations were evaluated in a set of 31 diverse maize inbred lines in three trials at two locations – Delhi (Kharif 2007 & 2008) and Hyderabad (Rabi 2007–08). The ranges of kernel Fe and Zn concentrations were 13.95–39.31 mg/kg and 21.85–40.91 mg/kg, respectively, across the three environments. Pooled analysis revealed significant genotype × environment (G × E) interaction in the expression of both the micronutrient traits, although kernel Fe was found to be more sensitive to G × E as compared to kernel Zn. Seven inbred lines, viz., BAJIM-06-03, DQPM-6, CM212, BAJIM-06-12, DQPM-7, DQPM-2 and CM129, were found to be the most stable and promising inbred lines for kernel Zn concentration, while for kernel Fe concentration, no promising and stable genotypes could be identified. Analysis of molecular diversity in 24 selected inbred lines with phenotypic contrast for the two kernel micronutrient traits, using 50 SSR markers covering the maize genome, revealed high levels of polymorphisms (214 SSR alleles; mean PIC value?=?0.62). The phenotypically contrasting and genetically diverse maize inbred lines identified in this study could be potentially utilized in further studies on QTL analysis of kernel micronutrient traits in maize, and the stable and most promising kernel micronutrient-rich maize genotypes provide a good foundation for developing micronutrient-enriched maize varieties suitable for the Indian context.     

Genomic models with genotype × environment interaction for predicting hybrid performance: an application in maize hybrids

Key message

A new genomic model that incorporates genotype?×?environment interaction gave increased prediction accuracy of untested hybrid response for traits such as percent starch content, percent dry matter content and silage yield of maize hybrids.

Abstract

The prediction of hybrid performance (HP) is very important in agricultural breeding programs. In plant breeding, multi-environment trials play an important role in the selection of important traits, such as stability across environments, grain yield and pest resistance. Environmental conditions modulate gene expression causing genotype?×?environment interaction (G?×?E), such that the estimated genetic correlations of the performance of individual lines across environments summarize the joint action of genes and environmental conditions. This article proposes a genomic statistical model that incorporates G?×?E for general and specific combining ability for predicting the performance of hybrids in environments. The proposed model can also be applied to any other hybrid species with distinct parental pools. In this study, we evaluated the predictive ability of two HP prediction models using a cross-validation approach applied in extensive maize hybrid data, comprising 2724 hybrids derived from 507 dent lines and 24 flint lines, which were evaluated for three traits in 58 environments over 12 years; analyses were performed for each year. On average, genomic models that include the interaction of general and specific combining ability with environments have greater predictive ability than genomic models without interaction with environments (ranging from 12 to 22%, depending on the trait). We concluded that including G?×?E in the prediction of untested maize hybrids increases the accuracy of genomic models.

     

燕麦种质资源重要农艺性状适应性和稳定性评价

为客观评价燕麦种质资源重要农艺性状的适应性和稳定性,本研究利用加权隶属函数法分析了81份燕麦种质材料在7个试验点的株高等7个重要农艺性状的遗传差异,以加权隶属函数值(D值)构建基因型×环境的GGE双标图,分析裸燕麦、皮燕麦在不同试验点的适应性和稳定性。结果表明:主穗粒重是裸燕麦材料在所有试验点中变异程度最大的性状,有效分蘖数是皮燕麦中变异程度最大的性状,其余5个性状的变异程度与皮裸性几乎无关;加权隶属函数法结合GGE双标图在对燕麦农艺性状进行综合分析时具有很好的应用价值;坝莜三号、73014-336、二莜麦、Bauntebue、坝燕一号等材料可用于实际生产,其中的坝莜三号、坝燕一号已是当下河北等地区的主栽品种;晋8609-1、LY03-02、二秋莜麦、64燕麦、品16、Banner、LY01-12等可作为杂交育种的亲本材料。     

Hybrid breeding of rice via genomic selection

Hybrid breeding is the main strategy for improving productivity in many crops, especially in rice and maize. Genomic hybrid breeding is a technology that uses whole‐genome markers to predict future hybrids. Predicted superior hybrids are then field evaluated and released as new hybrid cultivars after their superior performances are confirmed. This will increase the opportunity of selecting true superior hybrids with minimum costs. Here, we used genomic best linear unbiased prediction to perform hybrid performance prediction using an existing rice population of 1495 hybrids. Replicated 10‐fold cross‐validations showed that the prediction abilities on ten agronomic traits ranged from 0.35 to 0.92. Using the 1495 rice hybrids as a training sample, we predicted six agronomic traits of 100 hybrids derived from half diallel crosses involving 21 parents that are different from the parents of the hybrids in the training sample. The prediction abilities were relatively high, varying from 0.54 (yield) to 0.92 (grain length). We concluded that the current population of 1495 hybrids can be used to predict hybrids from seemingly unrelated parents. Eventually, we used this training population to predict all potential hybrids of cytoplasm male sterile lines from 3000 rice varieties from the 3K Rice Genome Project. Using a breeding index combining 10 traits, we identified the top and bottom 200 predicted hybrids. SNP genotypes of the training population and parameters estimated from this training population are available for general uses and further validation in genomic hybrid prediction of all potential hybrids generated from all varieties of rice.     

Quantitative genetic parameters of agronomic and quality traits in a global germplasm collection reveal excellent breeding perspectives for Jatropha curcas L.

Jatropha curcas L. (jatropha) is an undomesticated plant, which has received great attention in recent years for its potential in biofuel production and in greening and rehabilitation of wastelands. Yet the absence of improved cultivars and the lack of agronomic knowledge are limiting factors for successful jatropha cultivation. The objectives of the present study were to investigate the perspectives of a worldwide jatropha breeding program and specifically to (i) estimate variance components and heritabilities for agronomic and quality traits in the early phase of cultivation; (ii) assess phenotypic and genetic correlations among those traits; and (iii) discuss strategies for breeding high yielding jatropha cultivars. Data on various traits was collected from 375 jatropha genotypes, which were tested at seven locations during the first 3 years of growth. The accumulated seed yields and the seed yields per harvest year differed significantly among the testing locations. The estimates of genetic and genotype‐by‐environment interaction variances were significant and estimates of heritabilities were high for all yield parameters. The estimates of genetic correlations indicated a strong association among yield parameters. Oil yield was strongly correlated with seed yield and only weakly with oil content in seeds. The perspectives of a jatropha breeding program are excellent. Improved cultivars, definition of favorable environmental factors and refinement of agronomic management practices are needed to secure sustainable jatropha cultivation.     

Assessment of sorghum genetic resources of Ethiopia for anthracnose (Colletotrichum sublineolum Henn.) resistance and agronomic traits

Sorghum anthracnose caused by Colletotrichum sublineolum Henn. is one of the key diseases limiting sorghum production and productivity. Development of anthracnose‐resistant sorghum genotypes possessing yield‐promoting agronomic traits is an important breeding goal in sorghum improvement programs. The objective of this study was to determine the responses of diverse sorghum genetic resources for anthracnose resistance and agronomic traits to identify desirable lines for breeding. A total of 366 sorghum collections and three standard checks were field evaluated during the 2016 and 2017 cropping seasons. Lines were artificially inoculated with a virulent pure isolate of the pathogen. Anthracnose disease severity was assessed to calculate the area under disease progress curve (AUDPC). Agronomic traits such as panicle length (PL), panicle width (PW), head weight (HW) and thousand grain weight (TGW) were measured. Lines showed highly significant differences (p < .001) for anthracnose severity, AUDPC and agronomic traits. Among the collections 32 lines developed levels of disease severity between 15% and 30% in both seasons. The following sorghum landraces were selected: 71708, 210903, 74222, 73955, 74685, 74670, 74656, 74183, 234112, 69412, 226057, 214852, 71420, 71484, 200126, 71557, 75120, 71547, 220014, 228179, 16212, 16173, 16133, 69088, 238388, 16168 and 71570. These landraces had a relatively low anthracnose severity possessing farmer‐preferred agronomic traits. The selected genotypes are useful genetic resources to develop anthracnose‐resistant sorghum cultivars.     

The effects of selection for sodium transport and of selection for agronomic characteristics upon salt resistance in rice (Oryza sativa L.)

A multiple cross was constructed with the aim of combining component traits for the complex salinity resistance character. The aim was to combine donors for physiological traits with the agronomically desirable semidwarf/intermediate plant type and with the overall salinity resistance of the traditional tall land races. We report a study of selection strategies in the resulting breeding population. The effects of early selection for agronomic traits and early selection for low sodium transport were compared with a control population in which minimal selection was practised. Conventional selection for agronomic characters at early generations selected against low sodium-transporting (and thus potentially salt-tolerant) genotypes. In contrast, mild early selection for low sodium transport enriched the population in potentially salt-resistant genotypes but did not select against agronomic (semi-dwarf/intermediate) genotypes. It is concluded that selection for agronomic traits should be made after selection for salt resistance and, ideally, should be delayed until the population has reached near-homozygosity.     

Laboratory-and Field-Phenotyping for Drought Stress Tolerance and Diversity Study in Lentil (<i>Lens culinaris</i> Medik.)

Drought susceptibility and low genetic variability are the major constraints of lentil (Lens culinaris Medik.) production worldwide. Development of an efficient pre-field drought phenotyping technique and identification of diversified drought tolerant lentil genotype(s) are therefore vital and necessary. Two separate experiments were conducted using thirty diverse lentil genotypes to isolate drought tolerant genotype(s) as well as to assess their diversity. In both of the experiments, significant (p ≤ 0.01) variation in genotype (G), treatment (T) and G X T was observed for most of the studied traits. In experiment I, genotypes were examined for drought tolerance at the seedlings stage under hydroponic conditions by assessing root and shoot traits. Among the 30 genotypes studied, BM-1247, BM-1227 and BM-502 were selected as highly tolerant to drought stress as they showed maximum seedling survivability and minimum reduction in growth parameters under drought stress. In experiment II, the genotypes were assayed for diversity and drought stress tolerance based on morphological traits grown under field condition. Drought stress caused a substantial reduction in yield attributing traits, however, the genotypes BM-1247, BM-981, BM-1227 and BM-502 were categorized as drought tolerant genotypes with less than 20% yield reduction. The field screening result of drought stress tolerance was coincided well with the results of laboratory screening. Genetic divergence study reflected the presence of considerable diversity among the genotypes. Considering laboratory and field screening results, the genotypes, BM-1247, BM-1227, BM-981 and BM- 502 were selected as the best drought tolerant genotypes. This information can be exploited for further breeding in developing drought tolerance in lentil.     

Identification of drought tolerant Chickpea genotypes through multi trait stability index

Drought is a major and constantly increasing abiotic stress factor, thus limiting chickpea production. Like other crops, Kabuli Chickpea genotypes are screened for drought stress through Multi-environment trials (METs). Although, METs analysis is generally executed taking into account only one trait, which provides less significant reliability for the recommendation of genotypes as compared to multi trait-based analysis. Multi trait-based analysis could be used to recommend genotypes across diverse environments. Hence, current research was conducted for selection of superior genotypes through multi-trait stability index (MTSI) by using mixed and fixed effect models under six diverse environments. The genotypic stability was computed for all traits individually using the weighted average of absolute scores from the singular value decomposition of the matrix of best linear unbiased predictions for the genotype vs environment interaction (GEI) effects produced by a linear mixed-effect model index. A superiority index, WAASBY was measured to reflect the MPS (Mean performance and stability). The selection differential for the WAASBY index was 11.2%, 18.49% and 23.30% for grain yield (GY), primary branches per plant (PBP) and Stomatal Conductance (STOMA) respectively. Positive selection differential (0.80% ≤ selection differential ≤ 13.00%) were examined for traits averaged desired to be increased and negative (-0.57% ≤ selection differential ≤ -0.23%) for those traits desired to be reduced. The MTSI may be valuable to the plant breeders for the selection of genotypes based on many characters as being strong and simple selection process. Analysis of MTSI for multiple environments revealed that, the genotypes G20, G86, G31, G28, G116, G12, G105, G45, G50, G10, G30, G117, G81, G48, G85, G17, G32, G4, and G37 were the most stable and high yielding out of 120 chickpea genotypes, probably due to high MPS of selected traits under various environments. It is concluded that identified traits can be utilized as genitors in hybridization programs for the development of drought tolerant Kabuli Chickpea breeding material.     

Field performance of transgenic tall fescue (<Emphasis Type="Italic">Festuca arundinacea</Emphasis> Schreb.) plants and their progenies

Tall fescue (Festuca arundinacea Schreb.) is a hexaploid, outcrossing grass species widely used for forage and turf purposes. Transgenic tall fescue plants were generated by biolistic transformation of embryogenic cell suspension cultures that were derived from single genotypes of widely used cultivar Kentucky-31. Primary transgenics from two genotypes, their corresponding regenerants from the same genotypes and control seed-derived plants were transferred to the field and evaluated for 2 years. Progenies of these three classes of plants were obtained and evaluated together with seed-derived plants in a second field experiment. The agronomic characteristics evaluated were: heading date, anthesis date, height, growth habit, number of reproductive tillers, seed yield and biomass. The agronomic performance of the primary transgenics and regenerants was generally inferior to that of the seed-derived plants, with primary transgenics having fewer tillers and a lower seed yield. However, no major differences between the progenies of transgenics and the progenies of seed-derived plants were found for the agronomic traits evaluated. Primary transgenics and regenerants from the same genotype were more uniform than plants from seeds. Progenies of transgenics performed similarly to progenies of the regenerants. The addition of a selectable marker gene in the plant genome seems to have had little effect on the agronomic performance of the regenerated plants. No indication of weediness of the transgenic tall fescue plants was observed. Our results indicate that outcrossing grass plants generated through transgenic approaches can be incorporated into forage breeding programs.     

Exploitation of yield stability in barley

Key message

Analyses of registration trials of winter barley suggested that yield and yield stability can be enhanced by developing hybrid instead of line varieties.

Abstract

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.     

Genetic variability and association of characters in linseed (Linum usitatissimum L.) plant grown in central Ethiopia region

Linseed is one of the most important oil seed crop in the central highlands of Ethiopia for which yield enhancement is the major breeding purposes and genotypic variability is important for selection in any breeding programs. However, shortage of improved varieties’ that provides optimum seed yield is one of the major constraints of the crop. Therefore, this study was carried out to assess the genetic variability and association among quantitative traits of 36 linseed genotypes. The experiment was conducted in 2018 main cropping season by using simple lattice design. The analysis of variances reveled highly significant difference among the genotype for most of traits considered in present study. High phenotypic and genotypic coefficient of variation was recorded for tiller per plant, harvest index, oil yield (kg ha⁻¹), and seed yield (ton ha-1) number of capsules per plant. High heritability along with genetic advance was observed for seed yield (tones ha-¹), oil yield (kg ha-¹) harvest index which indicates selection of these traits at early generation would be effective. Oil yield (kg ha⁻¹) harvest index and number of capsules plant ⁻¹ showed highly significant positive with seed yield (ton ha⁻¹). Cluster analysis revealed that 36 linseed genotypes were grouped into two clusters and four genotypes remain ungrouped. The maximum inter clusters distance was observed between clusters II and the local check. The data set was reduced into four significant principal components (PCs) that comprise (80%) of the variance. The first PC accounted for 34% of the variances that implies greater proportion of variable information explained by PC1. The traits, which contributed more to PC1, were seed yield per plant, primary branches per plant, secondary branches per plant and plant height showed positive association and had positive direct effect on seed yield. This indicates that any improvement of oil yield and harvest index would result in substantial increase on seed.     

Towards a better understanding of the genetic and physiological basis for nitrogen use efficiency in maize

To enhance our understanding of the genetic basis of nitrogen use efficiency in maize (Zea mays), we have developed a quantitative genetic approach by associating metabolic functions and agronomic traits to DNA markers. In this study, leaves of vegetative recombinant inbred lines of maize, already assessed for their agronomic performance, were analyzed for physiological traits such as nitrate content, nitrate reductase (NR), and glutamine synthetase (GS) activities. A significant genotypic variation was found for these traits and a positive correlation was observed between nitrate content, GS activity and yield, and its components. NR activity, on the other hand, was negatively correlated. These results suggest that increased productivity in maize genotypes was due to their ability to accumulate nitrate in their leaves during vegetative growth and to efficiently remobilize this stored nitrogen during grain filling. Quantitative trait loci (QTL) for various agronomic and physiological traits were searched for and located on the genetic map of maize. Coincidences of QTL for yield and its components with genes encoding cytosolic GS and the corresponding enzyme activity were detected. In particular, it appears that the GS locus on chromosome 5 is a good candidate gene that can, at least partially, explain variations in yield or kernel weight. Because at this locus coincidences of QTLs for grain yield, GS, NR activity, and nitrate content were also observed, we hypothesize that leaf nitrate accumulation and the reactions catalyzed by NR and GS are coregulated and represent key elements controlling nitrogen use efficiency in maize.     

Breeding maize as biogas substrate in Central Europe: I. Quantitative-genetic parameters for testcross performance

Biofuels have gained importance recently and the use of maize biomass as substrate in biogas plants for production of methane has increased tremendously in Germany. The objectives of our research were to (1) estimate variance components and heritability for different traits relevant to biogas production in testcrosses (TCs) of maize, (2) study correlations among traits, and (3) discuss strategies to breed maize as a substrate for biogas fermenters. We evaluated 570 TCs of 285 diverse dent maize lines crossed with two flint single-cross testers in six environments. Data were recorded on agronomic and quality traits, including dry matter yield (DMY), methane fermentation yield (MFY), and methane yield (MY), the product of DMY and MFY, as the main target trait. Estimates of variance components showed general combining ability (GCA) to be the major source of variation. Estimates of heritability exceeded 0.67 for all traits and were even much greater in most instances. Methane yield was perfectly correlated with DMY but not with MFY, indicating that variation in MY is primarily determined by DMY. Further, DMY had a larger heritability and coefficient of genetic variation than MFY. Hence, for improving MY, selection should primarily focus on DMY rather than MFY. Further, maize breeding for biogas production may diverge from that for forage production because in the former case, quality traits seem to be of much lower importance.