Prospects for increasing yield in macadamia using component traits and genomics

Selection of candidate cultivars in macadamia requires extensive phenotypic measurements over many years and trials. In particular, yield traits such as nut-in-shell yield and kernel yield are economically vital characteristics and therefore guide the selection process for new cultivars. However, these traits can only be measured in mature trees, resulting in long generation intervals and slow rates of genetic gain. In addition, these traits are expensive to measure. Strategies to reduce the generation interval and increase the intensity of selection include using yield component traits, identification of markers associated with component traits, and genomic selection for yield. Yield component traits that contribute to resource availability for fruit formation include floral and nut characteristics. In this review, these traits will be investigated to estimate their relative importance in macadamia breeding and their heritability and correlations with yield. Furthermore, the usefulness of genome-wide association studies regarding yield component traits will be reviewed. Genetic-based breeding techniques could exploit this information to increase yield gains per breeding cycle and estimate the quantitative nature of yield traits. Genomic selection uses genome-wide molecular markers to predict the phenotype of individuals at an early age before maturity, thereby reducing the cycle time and increasing gain per unit time in plant breeding programmes. This review evaluates the potential for measurement of yield component traits, genome-wide association studies, and genomic selection to be employed in the Australian macadamia breeding programme to accelerate gains for nut yield.     

Multiple-trait multiple-country genetic evaluation of Holstein bulls for female fertility and milk production traits

The aim of this study was to investigate the effect of including milk yield data in the international genetic evaluation of female fertility traits to reduce or eliminate a possible bias because of across-country selection for milk yield. Data included two female fertility traits from Great Britain, Italy and the Netherlands, together with milk yield data from the same countries and from the United States, because the genetic trends in other countries may be influenced by selection decisions on bulls in the United States. Potentially, female fertility data had been corrected nationally for within-country selection and management biases for milk yield. Using a multiple-trait multiple across-country evaluation (MT-MACE) for the analysis of female fertility traits with milk yield, across-country selection patterns both for female fertility and milk yield can be considered simultaneously. Four analyses were performed; one single-trait multiple across-country evaluation analysis including only milk yield data, one MT-MACE analysis including only female fertility traits, and one MT-MACE analysis including both female fertility and milk yield traits. An additional MT-MACE analysis was performed including both female fertility and milk yield traits, but excluding the United States. By including milk yield traits to the analysis, female fertility reliabilities increased, but not for all bulls in all the countries by trait combinations. The presence of milk yield traits in the analysis did not considerably change the genetic correlations, genetic trends or bull rankings of female fertility traits. Even though the predicted genetic merits of female fertility traits hardly changed by including milk yield traits to the analysis, the change was not equally distributed to the whole data. The number of bulls in common between the two sets of Top 100 bulls for each trait in the two analyses of female fertility traits, with and without the four milk yield traits and their rank correlations were low, not necessarily because of the absence of the US milk yield data. The joint international genetic evaluation of female fertility traits with milk yield is recommended to make use of information on several female fertility traits from different countries simultaneously, to consider selection decisions for milk yield in the genetic evaluation of female fertility traits for obtaining more accurate estimating breeding values (EBV) and to acquire female fertility EBV for bulls evaluated for milk yield, but not for female fertility.     

Accelerating the Switchgrass (Panicum virgatum L.) Breeding Cycle Using Genomic Selection Approaches

Switchgrass (Panicum virgatum L.) is a perennial grass undergoing development as a biofuel feedstock. One of the most important factors hindering breeding efforts in this species is the need for accurate measurement of biomass yield on a per-hectare basis. Genomic selection on simple-to-measure traits that approximate biomass yield has the potential to significantly speed up the breeding cycle. Recent advances in switchgrass genomic and phenotypic resources are now making it possible to evaluate the potential of genomic selection of such traits. We leveraged these resources to study the ability of three widely-used genomic selection models to predict phenotypic values of morphological and biomass quality traits in an association panel consisting of predominantly northern adapted upland germplasm. High prediction accuracies were obtained for most of the traits, with standability having the highest ten-fold cross validation prediction accuracy (0.52). Moreover, the morphological traits generally had higher prediction accuracies than the biomass quality traits. Nevertheless, our results suggest that the quality of current genomic and phenotypic resources available for switchgrass is sufficiently high for genomic selection to significantly impact breeding efforts for biomass yield.     

Potential for Marker-Assisted Simultaneous Improvement of Grain and Biomass Yield in Triticale

Triticale is a promising crop for agricultural biomass production but breeding has until now mainly focused on grain yield. Here, we evaluated the potential of marker-assisted simultaneous improvement of grain yield and biomass yield. To this end, we employed a large triticale doubled haploid population with 647 individuals derived from four families that were phenotyped for grain yield and biomass yield, as well as thousand-kernel weight, tiller density, and plant height in multi-environment field trials. Employing an association mapping approach, we identified quantitative trait loci (QTL) for all the five traits. The phenotypic correlation between grain yield and biomass yield was low, and we detected only one overlapping QTL suggesting different genetic architectures underlying both traits. Our results indicate that a marker-based selection for either grain yield or biomass yield does not adversely affect the other traits. Furthermore, an improvement of the multiplicative yield traits can to some extent also be achieved by selection for QTL identified for the component traits. Taken together, our results suggest that marker-assisted breeding can assist the establishment of dual-purpose triticale cultivars with high grain and biomass yield.     

Tradeoffs between productivity and nitrogen conservation in wild and domesticated plants of the perennial crop Physaria (Brassicaceae)

Domestication of desert‐adapted perennials has been proposed as an alternative to increase cropping intensity in low input environments. However, selection for high yield in perennial crops provokes indirect changes in plant resource use. These changes might alter how nitrogen is acquired, allocated, used and stored, thus affecting plant longevity and yield stability. Using the perennial forb Physaria as a model, we compared the pattern of N allocation, use and conservation of high‐yield accessions (Y), and stable yield accessions (S), with their wild counterpart (W). We found a negative relationship between seed yield and N conservation capacity and their associated traits. The shift of strategy provoked by high yield selection implies changes in N allocation and a decrease in nitrogen use efficiency and conservation capacity at plant‐ and leaf‐level. S accessions also increase allocation to seeds, although they kept the N use and conservation traits of their wild counterparts, remaining in an intermediate position between the Y and W accessions profiles. The shift of strategy provoked by high yield selection implies a decrease in nitrogen use efficiency and conservation capacity that may be behind the loss of longevity and yield on subsequent years. Wild and stable accessions were more conservative and show traits that promote nitrogen conservation. These are key traits that should not be lost during the selection process if breeders want to achieve the adequate ideotype of perennial crop for arid systems.     

Assessment of Stalk Properties to Predict Juice Yield in Sorghum

Sweet sorghum is an outstanding feedstock choice for bioethanol production, but the gap between theoretical and commercial ethanol yields must be reduced to improve economic viability. Extractable juice yield is a primary limiting factor for higher ethanol yield, but current phenotyping techniques to measure juice yield in sorghum can be laborious. Therefore, alternative approaches to measuring juice yield during selection are needed. The objectives of this study were to investigate the relationship between stalk-related traits and juice yield and to assess the ability to predict juice yield using agronomic traits and stalk properties across and within a diverse set of sorghum ideotypes (photoinsensitive, photosensitive, biomass, grain, and sweet types). Stalk weight, stalk volume, stalk diameter, and plant height had significantly strong associations with juice yield, which were consistent across different sorghum ideotypes. The direct and indirect effects of multiple predictive traits on juice yield varied greatly with the distinct sorghum subsets. However, equation modeling demonstrated that juice yield is satisfactorily predicted by jointly assessing stalk weight and stalk moisture. Moreover, alternative prediction models involving distinct combinations of agronomic and stalk-related traits had similarly good prediction accuracy. Altogether, this suggests that several prediction models can be used to accelerate phenotyping for juice yield, which will improve the selection process. Overall, the results indicate that increasing sorghum juice yield via indirect selection is possible, but the choice of prediction model depends on the ideotypes and resources available in a breeding program.     

Mapping quantitative trait loci controlling variation in forage quality traits in barley

Barley forage quality has a direct relationship to animal performance, but forage quality traits are often neglected or not accessible to the plant breeders. Doubled haploid lines (145) from the cross Steptoe × Morex were grown in 2 years of trails under irrigated conditions to evaluate the variation in forage quality characteristics, identify quantitative trait loci (QTL) for these traits and determine if variation in forage quality characteristics among barley lines is heritable. Forage quality traits were determined at plant anthesis and at peak forage yield stages. A total of 32 QTL were identified that conditioned forage traits at anthesis stage, and 10 QTLs were identified at peak forage yield. At anthesis, forage traits were highly to moderate heritablely, while at peak forage yield, all traits were weakly heritable, indicating that selection progress for these traits will be effective at early stages of maturity. This research has identified and mapped QTL for barley forage quality and will allow deployment of genes for improved forage quality via marker-assisted selection.     

Molecular mapping of genomic regions harbouring QTLs for root and yield traits in sorghum (Sorghum bicolor L. Moench)

Root system is a vital part of plants for absorbing soil moisture and nutrients and it influences the drought tolerance. Identification of the genomic regions harbouring quantitative trait loci (QTLs) for root and yield traits, and the linked markers can facilitate sorghum improvement through marker-assisted selection (MAS) besides the deeper understanding of the plant response to drought stress. A population of 184 recombinant inbred lines (RILs), derived from E36-1 × SPV570, along with parents were phenotyped for component traits of yield in field and root traits in an above ground rhizotron. High estimates of heritability and genetic advance for all the root traits and for most of the yield traits, presents high scope for improvement of these traits by simple selection. A linkage map constructed with 104 marker loci comprising 50 EST-SSRs, 34 non-genic nuclear SSRs and 20 SNPs, and QTL analysis was performed using composite interval mapping (CIM) approach. A total of eight and 20 QTLs were mapped for root and yield related traits respectively. The QTLs for root volume, root fresh weight and root dry weight were found co-localized on SBI-04, supported by a positive correlation among these traits. Hence, these traits can be improved using the same linked markers. The lack of overlap between the QTLs of component traits of root and yield suggested that these two sets of parameters are independent in their influence and the possibility of combining these two traits might enhance productivity of sorghum under receding moisture condition.     

Mapping QTLs for improving grain yield using the USDA rice mini-core collection

Yield is the most important and complex trait for genetic improvement in crops, and marker-assisted selection enhances the improvement efficiency. The USDA rice mini-core collection derived from over 18,000 accessions of global origins is an ideal panel for association mapping. We phenotyped 203 O. sativa accessions for 14 agronomic traits and identified 5 that were highly and significantly correlated with grain yield per plant: plant height, plant weight, tillers, panicle length, and kernels/branch. Genotyping with 155 genome-wide molecular markers demonstrated 5 main cluster groups. Linkage disequilibrium (LD) decayed at least 20 cM and marker pairs with significant LD ranged from 4.64 to 6.06% in four main groups. Model comparisons revealed that different dimensions of principal component analysis affected yield and its correlated traits for mapping accuracy, and kinship did not improve the mapping in this collection. Thirty marker–trait associations were highly significant, 4 for yield, 3 for plant height, 6 for plant weight, 9 for tillers, 5 for panicle length and 3 for kernels/branch. Twenty-one markers contributed to the 30 associations, because 8 markers were co-associated with 2 or more traits. Allelic analysis of OSR13, RM471 and RM7003 for their co-associations with yield traits demonstrated that allele 126 bp of RM471 and 108 bp of RM7003 should receive greater attention, because they had the greatest positive effect on yield traits. Tagging the QTLs responsible for multiple yield traits may simultaneously help dissect the complex yield traits and elevate the efficiency to improve grain yield using marker-assisted selection in rice.     

Breeding cereals for Mediterranean conditions: ecophysiological clues for biotechnology application

Water stress is the main environmental factor limiting cereal yield in Mediterranean environments. For particular regions, such as the Mediterranean basin, the agroecological conditions are expected to get worse. In response to this challenge attempts are being made to improve crop yield through farm‐ management practices and plant breeding efforts. Here we examine traits that may be used as selection criteria for breeding C3 cereal crops with improved yield and stability in Mediterranean conditions. Emphasis is made on the potential implications of defining proper selection traits and target environments when adopting biotechnology approaches in breeding programmes.     

Drought tolerance in rice: morphological and molecular genetic consideration

Rice is one of the most important food crop drastically affected by drought in lowland rice ecosystem. Dissecting out the traits of importance and genomic regions influencing the response of drought tolerance and yield traits on grain yield will aid the breeders to know the genetic mechanism of drought tolerance of rice leads to the development of drought tolerant varieties. Grain yield and its components on drought situation of recombinant inbred population (IR 58821/IR 52561) were investigated under lowland managed stress situation in 2003 and 2004 by given importance to the relative water content. Water deficit resulted in significant effect on phenology and grain yield. Best lines were selected for further varietal development programme. Variability studies showed the traits viz., days to 70% relative water content, leaf rolling, leaf drying, harvest index, biomass yield and grain yield offer high scope for improvement for drought tolerance by way of simple selection technique. Correlation and path analysis indicated that, to harness high yielding combined with drought tolerance breeders should give selection pressure on relative water content, panicle length, grains per panicle, harvest index, biomass yield, root/shoot ratio and root length in positive direction, and low scores of leaf rolling, leaf drying and drought recovery rate. Analysis of quantitative trait loci for drought tolerance, yield and its components allowed the identification of 38 regions associated with both drought tolerant and yield traits. Out of these, 18 were closely linked with DNA markers could be used for marker assisted selection in breeding for drought tolerance in rice. Pleiotropism and G × E effects interaction were noticed in some of the traits. Parent IR 58821 contributed favorable alleles for the entire drought related and most of the yield component traits. Identification of traits of importance and their nature of relationship by morphological and molecular level under lowland condition will be useful to improve drought tolerance of rice.     

Divergent Selection for Secondary Traits in Upland Tetraploid Switchgrass and Effects on Sward Biomass Yield

Switchgrass (Panicum virgatum L.) is currently undergoing intensive breeding efforts to improve biomass yield. Consideration must be made regarding the relative importance of spaced plantings to sward plots for evaluation and selection for increased biomass yield. It has previously been suggested that selection schemes using secondary plant morphological traits as selection criteria within spaced plantings may be an efficient method of making genetic gain. The objective of this study was to empirically test the effects of direct selection for plant height, tiller count, flowering date, and visual selection for biomass yield within spaced plantings on biomass yield and morphology traits within sward plots. Divergently selected populations for each trait were developed from the WS4U upland tetraploid germplasm and evaluated for biomass yield at five locations in Wisconsin during two growing seasons. Significant variation was observed between maternal parents of the selected populations for both selected and nonselected traits. Despite substantial differences between parent plant populations for plant morphology, significant differences were not observed for sward-plot biomass yield or sward-plot morphology relative to the base population. Late flowering selections yielded 2.0 Mg/ha greater biomass than early flowering selections (29 % increase). Plant height within sward plots was observed to have a strong positive correlation with biomass yield. Tiller count was observed to have a weak correlation with biomass yield. Based on the observed results, it is recommended that greater emphasis be placed on evaluation of biomass yield using sward plots.     

Genetic correlations and their dependence on environmental similarity—Insights from livestock data

Genetic correlations for a trait across environments are predicted to decrease as environments diverge. However, estimates of genetic correlations from natural populations are typically defined across a limited environmental range and prone to very large standard errors, making it difficult to test this prediction. We address the importance of environmental distance on genetic correlations by employing data from domestic cattle in which abundant and accurate estimates are available from a wide range of environments. Three production traits related to milk yield show a clear decrease in genetic correlations with increasing environmental divergence. This pattern was also evident for growth traits and other yield traits but not for traits related to reproduction, morphology, physiology, or disease. We suspect that this reflects weaker selection on these latter trait classes compared to production traits, or alternatively the effects of selection are constrained by unfavorable genetic correlations between traits. The results support the notion that traits that historically have been under strong directional selection in a small range of frequently encountered environments will evolve high genetic correlations across these environments, while exposure to uncommon (and dissimilar) environments lead to a reranking of gene effects and a decrease in genetic correlations across environments.     

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.     

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.

     

Exploration of Genetic Pattern of Phenological Traits in Wheat (<i>Triticum aestivum</i> L.) under Drought Stress

Drought is the major detrimental environmental factor for wheat (Triticum aestivum L.) production. The exploration of genetic patterns underlying drought tolerance is of great significance. Here we report the gene actions controlling the phenological traits using the line × tester model studying 27 crosses and 12 parents under normal irrigation and drought conditions. The results interpreted via multiple analysis (mean performance, correlations, principal component, genetic analysis, heterotic and heterobeltiotic potential) disclosed highly significant differences among germplasm. The phenological waxiness traits (glume, boom, and sheath) were strongly interlinked. Flag leaf area exhibits a positive association with peduncle and spike length under drought. The growing degree days (heat-units) greatly influence spikelets and grains per spike, however, the grain yield/plant was significantly reduced (17.44 g to 13.25 g) under drought. The principal components based on eigenvalue indicated significant PCs (first-seven) accounted for 79.9% and 73.9% of total variability under normal irrigation and drought, respectively. The investigated yield traits showed complex genetic behaviour. The genetic advance confronted a moderate to high heritability for spikelets/spike and grain yield/plant. The traits conditioned by dominant genetic effects in normal irrigation were inversely controlled by additive genetic effects under drought and vice versa. The magnitude of dominance effects for phenological and yield traits, i.e., leaf twist, auricle hairiness, grain yield/plant, spikelets, and grains/spike suggests that selection by the pedigree method is appropriate for improving these traits under normal irrigation conditions and could serve as an indirect selection index for improving yield-oriented traits in wheat populations for drought tolerance. However, the phenotypic selection could be more than effective for traits conditioned by additive genetic effects under drought. We suggest five significant cross combinations based on heterotic and heterobeltiotic potential of wheat genotypes for improved yield and enhanced biological production of wheat in advanced generations under drought.     

Genetic parameters in a rubber tree population: heritabilities,genotype-by-environment interactions and multi-trait correlations

Rubber tree breeding programs are mainly driven by selection of individuals with high yield and quality of rubber. Data from 51 open-pollinated progenies tested on six sites in Brazil were analyzed over several traits to estimate the following: genetic parameters such as narrow-sense heritability and additive genetic variance in single- and multi-site analyses, type B correlations to determine the relevance of genotype-by-environment interactions and its effects on alternative selection strategies, additive genetic repeatability correlation for rubber yield based on three consecutive yearly measurements, and type A correlations to evaluate trait-to-trait genetic associations for all measured traits. Average rubber yield (RYm) showed an estimated narrow-sense heritability of 0.31, with an estimated type B correlation of 0.84, indicating low levels of genotype-by-environment interaction. The trait survival and number of latex vessel rings (RG) showed larger genotype-by-environment interaction and the lowest heritabilites. High to moderate type B correlation was found for most traits, with a value of 0.85 between diameter (or girth) and RYm; therefore, it is possible to achieve interesting rubber yield genetic gains (over 3 years of measurements) from indirect selection based on diameter at age 2.     

High yields in advanced lines of Pima cotton are associated with higher stomatal conductance, reduced leaf area and lower leaf temperature

Advanced lines of Pima cotton ( Gossypium barbadense L.) bred for higher yield potential and heat resistance have higher stomata conductance and smaller leaf areas than those of obsolete lines. In controlled experiments, five commercial lines of Pima cotton having increasing lint yield and heat resistance showed a gradient of increasing stomatal conductance and decreasing leaf size. In field experiments, heat-sensitive, low yield Pima lines showed a lower stomatal conductance than high yielding, advanced lines. This indicates that selection for high yield potential and heat resistance has imposed a selection pressure for higher stomatal conductance and smaller leaf areas. The higher stomatal conductance and smaller leaf area in the advanced lines resulted in a lower leaf temperature in both controlled environments and in the field. The largest leaf temperature differences between obsolete and advanced lines were observed in the afternoon. These differences coincided with the largest differences in stomatal conductance and the highest air temperatures. Measurements of stomatal conductance and leaf temperature in field-grown progeny from a cross between the advanced line, Pima S-6. and the obsolete line, Pima 32, showed that genetically determined differences in stomatal conductance resulted in corresponding differences in leaf temperature. None of the altered physiological traits were selected for in the breeding program, indicating that selection for the desired agronomic traits imposed selection pressures on the altered physiological traits. The increases in stomatal conductance and decreases in leaf area could represent an integrated response to selection pressures on enhanced evaporative cooling, ensuing from selection for heat resistance.     

Estimating genetic parameters of architectural and reproductive traits in young macadamia cultivars

Macadamia in the orchard environment is relatively unaltered from its natural form, and there is considerable scope to alter vegetative and reproductive architecture with the aim of improving yield efficiency through breeding. An understanding of the genetic and environmental control of architecture and the dynamics between vegetative and reproductive characteristics are fundamental to identifying traits for yield improvement. This experiment calculated the broad-sense heritability (H) of architectural traits and genetic correlations between vegetative and reproductive traits over 2 years. Cutting-grown clones (n?=?3) of 15 macadamia genotypes were subsampled from a randomised high-density plot planted in 2011 in South East Queensland, Australia, and observed for two seasons between 2015 and 2017. Clonal values from independent linear mixed models for multiple traits were combined in principal component analysis (PCA) to provide an insight to potential genetic relationships between traits, and genetic correlations were calculated from multivariate linear mixed model analysis. At the tree scale, canopy volume and yield had low H (0.07 and 0.14, respectively), implying a strong environmental influence on these complex traits. Architectural components of canopy volume and yield were considered at multiple scales, for which H ranged from 0.06 to 0.68, suggesting architectural traits at lower scales may be manipulated by breeding. Specific traits displayed strong genetic relationships with yield and canopy volume, suggesting that indirect selection for yield efficiency may be possible via selection for architectural and floral traits, to improve future macadamia cultivars.     

Genomic selection for grain yield and quality traits in durum wheat

The prediction accuracies of genomic selection depend on several factors, including the genetic architecture of target traits, the number of traits considered at a given time, and the statistical models. Here, we assessed the potential of single-trait (ST) and multi-trait (MT) genomic prediction models for durum wheat on yield and quality traits using a breeding panel (BP) of 170 varieties and advanced breeding lines, and a doubled-haploid (DH) population of 154 lines. The two populations were genotyped with the Infinium iSelect 90K SNP assay and phenotyped for various traits. Six ST-GS models (RR-BLUP, G-BLUP, BayesA, BayesB, Bayesian LASSO, and RKHS) and three MT prediction approaches (MT-BayesA, MT-Matrix, and MT-SI approaches which use economic selection index as a trait value) were applied for predicting yield, protein content, gluten index, and alveograph measures. The ST prediction accuracies ranged from 0.5 to 0.8 for the various traits and models and revealed comparable prediction accuracies for most of the traits in both populations, except BayesA and BayesB, which better predicted gluten index, tenacity, and strength in the DH population. The MT-GS models were more accurate than the ST-GS models only for grain yield in the BP. Using BP as a training set to predict the DH population resulted in poor predictions. Overall, all the six ST-GS models appear to be applicable for GS of yield and gluten strength traits in durum wheat, but we recommend the simple computational models RR-BLUP or G-BLUP for predicating single trait and MT-SI for predicting yield and protein simultaneously.