Stalk Rot Diseases Impact Sweet Sorghum Biofuel Traits

Owing to its sugar-rich stalks and high biomass, sweet sorghum [Sorghum bicolor (L.) Moench] has potential as a source of biofuel feedstock for juice and lignocellulosic-based bioethanol production. However, stalk rot-mediated lodging is an important concern. The potential impacts of disease on sweet sorghum biofuel traits are currently unknown. The objectives of this study were to test the effects of Fusarium stalk rot and charcoal rot on sweet sorghum biofuel traits and to assess the combining ability of the parental genotypes for resistance to the two diseases. Nineteen genotypes including 7 parents and 12 hybrids were tested in the field in 2014 (Ashland, Kansas) and 2015 (Manhattan, Kansas) against Fusarium thapsinum (FT) and Macrophomina phaseolina (MP). Fourteen days after flowering, plants were inoculated with FT and MP. Plants were harvested at 35 days after inoculation and measured for disease severity using stalk lesion length. Grain weight, juice weight, Brix (°Bx), and dried bagasse weight were also determined. Total soluble sugars per plant (TSSP) were determined using juice weight and °Bx. On average, FT and MP resulted in reduced grain weight and dried bagasse weight by 17.4 and 17.6 %, respectively, across genotypes. Depending on the genotype, pathogens reduced juice weight, °Bx, and TSSP in the ranges of 11.3 to 25.9, 0.2 to 16.7, and 21.2 to 33.3 %, respectively. Parental line general and specific combining abilities were found to be statistically insignificant. This study revealed the adverse effects of stalk rot diseases on harvestable biofuel traits and the need to breed sweet sorghum for stalk rot resistance.     

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.     

Impacts of main factors on bioethanol fermentation from stalk juice of sweet sorghum by immobilized Saccharomyces cerevisiae (CICC 1308)

In order to attain a higher ethanol yield and faster ethanol fermentation rate, orthogonal experiments of ethanol fermentation with immobilized yeast from stalk juice of sweet sorghum were carried out in the shaking flasks to investigate the effect of main factors, namely, fermentation temperature, agitation rate, particles stuffing rate and pH on ethanol yield and CO(2) weight loss rate. The range analysis and analysis of variance (ANOVA) were applied for the results of orthogonal experiments. Results showed that the optimal condition for bioethanol fermentation should be A(4)B(3)C(3)D(4), namely, fermentation temperature, agitation rate, particles stuffing rate and pH were 37 degrees C, 200rpm, 25% and 5.0, respectively. The verification experiments were carried out in shaking flasks and 5L bioreactor at the corresponding parameters. The results of verification experiments in the shaking flasks showed that ethanol yield and CO(2) weight loss rate were 98.07% and 1.020gh(-1), respectively. The results of ethanol fermentation in the 5L bioreactor showed that ethanol yield and fermentation time were 93.24% and 11h, respectively. As a result, it could be concluded that the determined optimal condition A(4)B(3)C(3)D(4) was suitable and reasonable for the ethanol fermentation by immobilized Saccharomyces cerevisiae. The conclusion in the research would be beneficial for application of ethanol fermentation by immobilized S. cerevisiae from stalk juice of sweet sorghum.     

Efficiency of multi-trait,indirect, and trait-assisted genomic selection for improvement of biomass sorghum

Key message

We compare genomic selection methods that use correlated traits to help predict biomass yield in sorghum, and find that trait-assisted genomic selection performs best.

Abstract

Genomic selection (GS) is usually performed on a single trait, but correlated traits can also help predict a focal trait through indirect or multi-trait GS. In this study, we use a pre-breeding population of biomass sorghum to compare strategies that use correlated traits to improve prediction of biomass yield, the focal trait. Correlated traits include moisture, plant height measured at monthly intervals between planting and harvest, and the area under the growth progress curve. In addition to single- and multi-trait direct and indirect GS, we test a new strategy called trait-assisted GS, in which correlated traits are used along with marker data in the validation population to predict a focal trait. Single-trait GS for biomass yield had a prediction accuracy of 0.40. Indirect GS performed best using area under the growth progress curve to predict biomass yield, with a prediction accuracy of 0.37, and did not differ from indirect multi-trait GS that also used moisture information. Multi-trait GS and single-trait GS yielded similar results, indicating that correlated traits did not improve prediction of biomass yield in a standard GS scenario. However, trait-assisted GS increased prediction accuracy by up to \(50\%\) when using plant height in both the training and validation populations to help predict yield in the validation population. Coincidence between selected genotypes in phenotypic and genomic selection was also highest in trait-assisted GS. Overall, these results suggest that trait-assisted GS can be an efficient strategy when correlated traits are obtained earlier or more inexpensively than a focal trait.

     

Genetic mapping of QTLs for sugar-related traits in a RIL population of Sorghum bicolor L. Moench

The productivity of sorghum is mainly determined by quantitative traits such as grain yield and stem sugar-related characteristics. Substantial crop improvement has been achieved by breeding in the last decades. Today, genetic mapping and characterization of quantitative trait loci (QTLs) is considered a valuable tool for trait enhancement. We have investigated QTL associated with the sugar components (Brix, glucose, sucrose, and total sugar content) and sugar-related agronomic traits (flowering date, plant height, stem diameter, tiller number per plant, fresh panicle weight, and estimated juice weight) in four different environments (two locations) using a population of 188 recombinant inbred lines (RILs) from a cross between grain (M71) and sweet sorghum (SS79). A genetic map with 157 AFLP, SSR, and EST-SSR markers was constructed, and several QTLs were detected using composite interval mapping (CIM). Further, additive × additive interaction and QTL × environmental interaction were estimated. CIM identified more than five additive QTLs in most traits explaining a range of 6.0–26.1% of the phenotypic variation. A total of 24 digenic epistatic locus pairs were identified in seven traits, supporting the hypothesis that QTL analysis without considering epistasis can result in biased estimates. QTLs showing multiple effects were identified, where the major QTL on SBI-06 was significantly associated with most of the traits, i.e., flowering date, plant height, Brix, sucrose, and sugar content. Four out of ten traits studied showed a significant QTL × environmental interaction. Our results are an important step toward marker-assisted selection for sugar-related traits and biofuel yield in sorghum.     

Efficient strategies to assess yield stability in winter wheat

Key message

Selecting contrasting environments allows decreasing phenotyping intensity but still maintaining high accuracy to assess yield stability.

Abstract

Improving yield stability of wheat varieties is important to cope with enhanced abiotic stresses caused by climate change. The objective of our study was to (1) develop and implement an improved heritability estimate to examine the required scale of phenotyping for assessing yield stability in wheat, (2) compare yield performance and yield stability of wheat hybrids and inbred lines, (3) investigate the association of agronomic traits with yield stability, and (4) explore the possibility of selecting subsets of environments allowing to portray large proportion of the variation of yield stability. Our study is based on phenotypic data from five series of official winter wheat registration trials in Germany each including 119–132 genotypes evaluated in up to 50 environments. Our findings suggested that phenotyping in at least 40 environments is required to reliably estimate yield stability to guarantee heritability estimates above 0.7. Contrasting the yield stability of hybrids versus lines revealed no significant differences. Absence of stable associations between yield stability and further agronomic traits suggested low potential of indirect selection to improve yield stability. Selecting posteriori contrasting environments based on the genotype-by-environment interaction effects allowed decreasing phenotyping intensity, but still maintaining high accuracy to assess yield stability. The huge potential of the developed strategy to select contrasting and informative environments has to be validated as a next step in an a priori scenario based on genotype-by-location interaction effects.

     

Assessment of Sugarcane Billet Harvester on Recovery of Sweet Sorghum Biomass for Ethanol Production

Sweet sorghum (Sorghum bicolor L. Moench) is a promising bioenergy crop for the production of ethanol and bio-based products. Sugarcane billet harvesters can be used to harvest sweet sorghum. Multiple extractor fan speed settings of these harvesters allow for separating the extraneous matter in the feedstock, which has been associated with increased milling throughput and better juice quality at the processing facility. This removal is not completely selective, and some stalk material is also lost. These losses can be higher for sweet sorghum than sugarcane due its lower weight. This paper presents an assessment of how the speed of the primary extractor fan of a sugarcane billet combine used for harvesting sweet sorghum affects the biomass yield, biomass losses, and quality at delivery for the production of ethanol from extracted juice and fiber. Three primary extractor fan speeds (0, 800, and 1100 rpm) were evaluated. Higher fan speeds decreased fresh biomass yields by up to 28.3 Mg ha^?1. Juice quality was not significantly different among treatments. Ethanol yield calculated from sweet sorghum harvested at 0 rpm was 6075 L ha^?1. This value decreased by about half for material harvested at 1100 rpm due to the differences in biomass yield.     

Genomic prediction applied to high-biomass sorghum for bioenergy production

The increasing cost of energy and finite oil and gas reserves have created a need to develop alternative fuels from renewable sources. Due to its abiotic stress tolerance and annual cultivation, high-biomass sorghum (Sorghum bicolor L. Moench) shows potential as a bioenergy crop. Genomic selection is a useful tool for accelerating genetic gains and could restructure plant breeding programs by enabling early selection and reducing breeding cycle duration. This work aimed at predicting breeding values via genomic selection models for 200 sorghum genotypes comprising landrace accessions and breeding lines from biomass and saccharine groups. These genotypes were divided into two sub-panels, according to breeding purpose. We evaluated the following phenotypic biomass traits: days to flowering, plant height, fresh and dry matter yield, and fiber, cellulose, hemicellulose, and lignin proportions. Genotyping by sequencing yielded more than 258,000 single-nucleotide polymorphism markers, which revealed population structure between subpanels. We then fitted and compared genomic selection models BayesA, BayesB, BayesCπ, BayesLasso, Bayes Ridge Regression and random regression best linear unbiased predictor. The resulting predictive abilities varied little between the different models, but substantially between traits. Different scenarios of prediction showed the potential of using genomic selection results between sub-panels and years, although the genotype by environment interaction negatively affected accuracies. Functional enrichment analyses performed with the marker-predicted effects suggested several interesting associations, with potential for revealing biological processes relevant to the studied quantitative traits. This work shows that genomic selection can be successfully applied in biomass sorghum breeding programs.     

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.     

High-throughput Phenotyping and Genomic Selection:The Frontiers of Crop Breeding Converge

Genomic selection (GS) and high-throughput phenotyping have recently been captivating the interest of the crop breeding com-munity from both the public and private sectors world-wide.Both approaches promise to revolutionize the prediction of complex traits,including growth,yield and adaptation to stress.Whereas high-throughput phenotyping may help to improve understanding of crop physiology,most powerful techniques for high-throughput field phenotyping are empirical rather than analytical and compa-rable to genomic selection.Despite the fact that the two method-ological approaches represent the extremes of what is understood as the breeding process (phenotype versus genome),they both consider the targeted traits (e.g.grain yield,growth,phenology,plant adaptation to stress) as a black box instead of dissecting them as a set of secondary traits (i.e.physiological) putatively related to the target trait.Both GS and high-throughput phenotyping have in common their empirical approach enabling breeders to use genome profile or phenotype without understanding the underlying biology.This short review discusses the main aspects of both approaches and focuses on the case of genomic selection of maize flowering traits and near-infrared spectroscopy (NIRS) and plant spectral reflectance as high-throughput field phenotyping methods for complex traits such as crop growth and yield.     

Markers associated with stalk number and suckering in sugarcane colocate with tillering and rhizomatousness QTLs in sorghum.

Two important factors influencing sugar yield, the primary focus of sugarcane plant breeding programs, are stalk number and suckering. Molecular markers linked to both of these traits are sought to assist in the identification of high sugar yield, high stalk number, low-suckering sugarcane clones. In this preliminary mapping study, 108 progeny from a biparental cross involving two elite Australian sugarcane clones were evaluated at two sites for two years for both stalk number and suckering. A total of 258 DNA markers, including both restriction fragment length polymorphisms (RFLPs) and radio-labelled amplified fragments (RAFs), were scored and evaluated using single-factor analysis. Sixteen (7 RFLPs and 9 RAFs) and 14 (6 RFLPs and 8 RAFs) markers were identified that were significantly associated (P < 0.01) with stalk number and suckering, respectively, across both years and sites. The seven and six RFLP markers associated with stalk number and suckering, respectively, were generated by eight different RFLP probes, of which seven had been mapped in sorghum and (or) sugarcane. Of significant interest was the observation that all seven RFLP probes could be shown to be located within or near QTLs associated with tillering and rhizomatousness in sorghum. This observation highlights the usefulness of comparative mapping between sorghum and sugarcane and suggests that the identification of useful markers for stalk number and suckering in sugarcane would be facilitated by focussing on sorghum QTLs associated with related traits.     

Evaluation of Public Sweet Sorghum A-Lines for Use in Hybrid Production

A fundamental need for commercialization of sweet sorghum [Sorghum bicolor (L.) Moench] as a bioenergy crop is an adequate seed supply, which will require development of hybrid varieties using dwarf seed-parent lines. A set of six public sweet sorghum A-lines (Dwarf Kansas Sourless, KS9, N36, N38, N39, and N4692) were crossed with a set of six public sweet sorghum cultivars (Brawley, Kansas Collier, Dale, Sugar Drip, Waconia, and Wray). Grain, fiber, and sugar yields were determined, and conversion formulas were applied to estimate ethanol yields. Hybrids were grown in fields at Ithaca, NE, USA, in 1983–1984 fertilized with 112 kg ha^?1 N. In terms of yield components and overall ethanol yields, one A-line, N38, was inferior. Average total ethanol yields from hybrids made on the other A-lines were not significantly different, suggesting that any of those five A-lines could be useful seed-parents. With the exception of grain yield, cultivars used as pollen parents were among the highest-performing entries for all traits. For all traits directly contributing to total ethanol yield (grain yield, juice yield, % soluble solids, sugar yield, fiber yield), hybrids were also among the highest-performing entries. Results of this study demonstrate that hybrid sweet sorghum with performance criteria equivalent to existing sweet sorghum cultivars can be produced on the sweet sorghum seed-parent lines A-Dwarf Kansas Sourless, A-KS9, A-N36, A-N39, and A-N4692. Identification of specific seed-parent × pollen parent lines with characteristics best suited for particular growing regions and end-user needs will be critical for commercial hybrid development.     

Juice,sugar, and bagasse response of sweet sorghum (Sorghum bicolor (L.) Moench cv. M81E) to N fertilization and soil type

The objective of this research was to determine the optimum nitrogen fertilizer rate for producing sweet sorghum (a promising biofuel crop) juice, sugar, and bagasse on silt loam, sandy loam, and clay soils in Missouri. Seven nitrogen fertilization rates were applied, ranging from 0 to 134 kg N ha^?1. Regardless of the soil and year, the juice content of sweet sorghum stalk averaged 68.8% by weight. The juice yield ranged from 15.2 to 71.1 m³ ha^?1. Soil and N rate significantly impacted the juice yield (P < 0.0001). The pH and the density of the juice were not affected by the soil or N. The sugar content (Brix) of the juice varied between 10.7% and 18.9%. N fertilization improved the sugar content of the juice. A negative correlation existed between the sugar concentration and the juice yield. In general, the lowest sugar content was found in the clay soil and the impact of the N fertilization on juice sugar content was most pronounced in that soil. The juice sugar yield ranged between 2 and 9.9 Mg ha^?1, with significant differences found between years, N rates, and soils. N fertilization always increased the sugar yield in the clay soil, whereas in loam soil, a significant sugar response was recorded when the sweet sorghum was planted after corn. The average juice water content was 84% by weight. The dry bagasse yield fluctuated between 3.2 and 13.8 Mg ha^?1 with significant difference found with N rate, soil, and year. When sweet sorghum was grown after soybean or cotton, its N requirement was less than after a corn crop was grown the previous year. In general, a minimum of 67 kg N ha^?1 was required to optimize juice, sugar, and bagasse yield in sweet sorghum.     

Longitudinal analyses of correlated response efficiencies of fillet traits in Nile tilapia

Recent studies with Nile tilapia have shown divergent results regarding the possibility of selecting on morphometric measurements to promote indirect genetic gains in fillet yield (FY). The use of indirect selection for fillet traits is important as these traits are only measurable after harvesting. Random regression models are a powerful tool in association studies to identify the best time point to measure and select animals. Random regression models can also be applied in a multiple trait approach to analyze indirect response to selection, which would avoid the need to sacrifice candidate fish. Therefore, the aim of this study was to investigate the genetic relationships between several body measurements, weight and fillet traits throughout the growth period and to evaluate the possibility of indirect selection for fillet traits in Nile tilapia. Data were collected from 2042 fish and was divided into two subsets. The first subset was used to estimate genetic parameters, including the permanent environmental effect for BW and body measurements (8758 records for each body measurement, as each fish was individually weighed and measured a maximum of six times). The second subset (2042 records for each trait) was used to estimate genetic correlations and heritabilities, which enabled the calculation of correlated response efficiencies between body measurements and the fillet traits. Heritability estimates across ages ranged from 0.05 to 0.5 for height, 0.02 to 0.48 for corrected length (CL), 0.05 to 0.68 for width, 0.08 to 0.57 for fillet weight (FW) and 0.12 to 0.42 for FY. All genetic correlation estimates between body measurements and FW were positive and strong (0.64 to 0.98). The estimates of genetic correlation between body measurements and FY were positive (except for CL at some ages), but weak to moderate (−0.08 to 0.68). These estimates resulted in strong and favorable correlated response efficiencies for FW and positive, but moderate for FY. These results indicate the possibility of achieving indirect genetic gains for FW and by selecting for morphometric traits, but low efficiency for FY when compared with direct selection.     

Quantitative trait loci (QTLs) for water use and crop production traits co-locate with major QTL for tolerance to water deficit in a fine-mapping population of pearl millet (<Emphasis Type="Italic">Pennisetum glaucum</Emphasis> L. R.Br.)

Key message

Four genetic regions associated with water use traits, measured at different levels of plant organization, and with agronomic traits were identified within a previously reported region for terminal water deficit adaptation on linkage group 2. Close linkages between these traits showed the value of phenotyping both for agronomic and secondary traits to better understand plant productive processes.

Abstract

Water saving traits are critical for water stress adaptation of pearl millet, whereas maximizing water use is key to the absence of stress. This research aimed at demonstrating the close relationship between traits measured at different levels of plant organization, some putatively involved in water stress adaptation, and those responsible for agronomic performance. A fine-mapping population of pearl millet, segregating for a previously identified quantitative trait locus (QTL) for adaptation to terminal drought stress on LG02, was phenotyped for traits at different levels of plant organization in different experimental environments (pot culture, high-throughput phenotyping platform, lysimeters, and field). The linkages among traits across the experimental systems were analysed using principal component analysis and QTL co-localization approach. Four regions within the LG02-QTL were found and revealed substantial co-mapping of water use and agronomic traits. These regions, identified across experimental systems, provided genetic evidence of the tight linkages between traits phenotyped at a lower level of plant organization and agronomic traits assessed in the field, therefore deepening our understanding of complex traits and then benefiting both geneticists and breeders. In short: (1) under no/mild stress conditions, increasing biomass and tiller production increased water use and eventually yield; (2) under severe stress conditions, water savings at vegetative stage, from lower plant vigour and fewer tillers in that population, led to more water available during grain filling, expression of stay-green phenotypes, and higher yield.

     

Multi-trait and multi-environment QTL analyses of yield and a set of physiological traits in pepper

Key message

A mixed model framework was defined for QTL analysis of multiple traits across multiple environments for a RIL population in pepper. Detection power for QTLs increased considerably and detailed study of QTL by environment interactions and pleiotropy was facilitated.

Abstract

For many agronomic crops, yield is measured simultaneously with other traits across multiple environments. The study of yield can benefit from joint analysis with other traits and relations between yield and other traits can be exploited to develop indirect selection strategies. We compare the performance of three multi-response QTL approaches based on mixed models: a multi-trait approach (MT), a multi-environment approach (ME), and a multi-trait multi-environment approach (MTME). The data come from a multi-environment experiment in pepper, for which 15 traits were measured in four environments. The approaches were compared in terms of number of QTLs detected for each trait, the explained variance, and the accuracy of prediction for the final QTL model. For the four environments together, the superior MTME approach delivered a total of 47 regions containing putative QTLs. Many of these QTLs were pleiotropic and showed quantitative QTL by environment interaction. MTME was superior to ME and MT in the number of QTLs, the explained variance and accuracy of predictions. The large number of model parameters in the MTME approach was challenging and we propose several guidelines to help obtain a stable final QTL model. The results confirmed the feasibility and strengths of novel mixed model QTL methodology to study the architecture of complex traits.     

Accuracy of genomic selection in European maize elite breeding populations

Genomic selection is a promising breeding strategy for rapid improvement of complex traits. The objective of our study was to investigate the prediction accuracy of genomic breeding values through cross validation. The study was based on experimental data of six segregating populations from a half-diallel mating design with 788 testcross progenies from an elite maize breeding program. The plants were intensively phenotyped in multi-location field trials and fingerprinted with 960 SNP markers. We used random regression best linear unbiased prediction in combination with fivefold cross validation. The prediction accuracy across populations was higher for grain moisture (0.90) than for grain yield (0.58). The accuracy of genomic selection realized for grain yield corresponds to the precision of phenotyping at unreplicated field trials in 3–4 locations. As for maize up to three generations are feasible per year, selection gain per unit time is high and, consequently, genomic selection holds great promise for maize breeding programs.     

Genomic Prediction of Testcross Performance in Canola (Brassica napus)

Genomic selection (GS) is a modern breeding approach where genome-wide single-nucleotide polymorphism (SNP) marker profiles are simultaneously used to estimate performance of untested genotypes. In this study, the potential of genomic selection methods to predict testcross performance for hybrid canola breeding was applied for various agronomic traits based on genome-wide marker profiles. A total of 475 genetically diverse spring-type canola pollinator lines were genotyped at 24,403 single-copy, genome-wide SNP loci. In parallel, the 950 F1 testcross combinations between the pollinators and two representative testers were evaluated for a number of important agronomic traits including seedling emergence, days to flowering, lodging, oil yield and seed yield along with essential seed quality characters including seed oil content and seed glucosinolate content. A ridge-regression best linear unbiased prediction (RR-BLUP) model was applied in combination with 500 cross-validations for each trait to predict testcross performance, both across the whole population as well as within individual subpopulations or clusters, based solely on SNP profiles. Subpopulations were determined using multidimensional scaling and K-means clustering. Genomic prediction accuracy across the whole population was highest for seed oil content (0.81) followed by oil yield (0.75) and lowest for seedling emergence (0.29). For seed yieId, seed glucosinolate, lodging resistance and days to onset of flowering (DTF), prediction accuracies were 0.45, 0.61, 0.39 and 0.56, respectively. Prediction accuracies could be increased for some traits by treating subpopulations separately; a strategy which only led to moderate improvements for some traits with low heritability, like seedling emergence. No useful or consistent increase in accuracy was obtained by inclusion of a population substructure covariate in the model. Testcross performance prediction using genome-wide SNP markers shows considerable potential for pre-selection of promising hybrid combinations prior to resource-intensive field testing over multiple locations and years.     

A Diploid,Interspecific, Fertile Hybrid from Cultivated Sorghum, <Emphasis Type="Italic">Sorghum Bicolor</Emphasis>, and the Common Johnsongrass Weed <Emphasis Type="Italic">Sorghum Halepense</Emphasis>

Valuable agronomic traits are often present but inaccessible in the wild relatives of cultivated crop species. Utilization of wild germplasm depends on the production of fertile interspecific hybrids. Several unsuccessful attempts have been made to hybridize cultivated sorghum with its wild relatives to broaden its genetic base and enhance agronomic value. The successful approach used in this study employed the nuclear male sterility gene ms3 to generate a diploid fertile hybrid between the diploid cultivated sorghum (Sorghum bicolor (L) Pers.) and its weedy tetraploid wild relative Johnsongrass (Sorghum halepense (L.) Pers.). Eight sorghum plants were selected from a Nebraska stiff stalk collection that contains the male sterility gene ms3 and were used as the female parent. About 36,000 florets of male sterile sorghum were pollinated with Johnsongrass pollen to produce an average of one well-developed and 180 severely shriveled seed/18,000 crosses. The well-developed seed gave rise to a self-fertile diploid, while none of the shriveled seed were able to germinate. The F₁ hybrid was confirmed by using cultivated sorghum SSR markers and was selfed to produce an F₂ population. A sub-sample of 96 segregating F₂ plants was examined with 36 sorghum polymorphic SSR markers. Thirty-four markers showed a normal 1:2:1 segregation ratio, evidence of normal recombination across the genome. Preliminary results showed that several desirable traits from Johnsongrass, including resistance to greenbug and chinch bug and adaptability to cold temperatures, were expressed in the resulting progenies. These observations suggest that speciation within the genus Sorghum, giving rise to widely divergent phenotypes, is effected largely by ploidy-maintained crossing barriers but apparently not by extensive genomic divergence.     

我国玉米自交系茎秆性状多样性分析

研究我国玉米自交系茎秆性状特征及其多样性,是培育宜机收玉米品种的重要前提。本研究以兰卡斯特、PB、四平头、旅大红骨和瑞德五大主要类群70份主要玉米自交系为材料,调查12个茎秆相关性状(茎高、穗位高、穗位系数、茎节数、穗位节、穗节系数、穗茎长、穗茎粗、茎鲜重、茎干重、含糖量和含水量),分析性状相关性和类群多样性。结果表明,我国地方种质四平头和旅大红骨茎秆性状表型变异丰富;灌浆期玉米茎秆含水量比较稳定;玉米植株高度与茎节长度显著相关;玉米雌、雄穗节之间的节间数比较恒定;玉米茎秆含糖量与茎节长度、茎粗、果穗着生位置有关;有效降低穗位高度应从降低果穗着生节入手;类群茎秆特征鲜明:兰卡斯特茎节较少,瑞德茎秆较粗,PB茎秆较细,旅大红骨茎秆较粗、茎节较短,四平头植株较矮、茎秆含糖量较低、干物质含量较低;兰卡斯特×四平头和兰卡斯特×PB类群间存在较强的生物量及籽粒产量杂种优势;挖掘和利用茎节较长、穗位较低的玉米地方种质是我国宜机收玉米育种的技术途径。本研究结果对玉米育种具有重要指导意义。