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Plant breeders regularly evaluate multiple traits across multiple environments, which opens an avenue for using multiple traits in genomic prediction models. We assessed the potential of multi-trait (MT) genomic prediction model through evaluating several strategies of incorporating multiple traits (eight agronomic and malting quality traits) into the prediction models with two cross-validation schemes (CV1, predicting new lines with genotypic information only and CV2, predicting partially phenotyped lines using both genotypic and phenotypic information from correlated traits) in barley. The predictive ability was similar for single (ST-CV1) and multi-trait (MT-CV1) models to predict new lines. However, the predictive ability for agronomic traits was considerably increased when partially phenotyped lines (MT-CV2) were used. The predictive ability for grain yield using the MT-CV2 model with other agronomic traits resulted in 57% and 61% higher predictive ability than ST-CV1 and MT-CV1 models, respectively. Therefore, complex traits such as grain yield are better predicted when correlated traits are used. Similarly, a considerable increase in the predictive ability of malting quality traits was observed when correlated traits were used. The predictive ability for grain protein content using the MT-CV2 model with both agronomic and malting traits resulted in a 76% higher predictive ability than ST-CV1 and MT-CV1 models. Additionally, the higher predictive ability for new environments was obtained for all traits using the MT-CV2 model compared to the MT-CV1 model. This study showed the potential of improving the genomic prediction of complex traits by incorporating the information from multiple traits (cost-friendly and easy to measure traits) collected throughout breeding programs which could assist in speeding up breeding cycles.

Grain protein content (GPC) is one of the most important quality traits in wheat, defining the nutritional and end-use properties and rheological characteristics. Over the years, a number of breeding programs have been developed aimed to improving GPC, most of them having been prevented by the negative correlation with grain yield. To overcome this issue, a collection of durum wheat germplasm was evaluated for both GPC and grain protein deviation (GPD) in seven field trials. Fourteen candidate genes involved in several processes related to nitrogen metabolism were precisely located on two high-density consensus maps of common and durum wheat, and six of them were found to be highly associated with both traits. The wheat collection was genotyped using the 90 K iSelect array, and 11 stable quantitative trait loci (QTL) for GPC were detected in at least three environments and the mean across environments by the genome-wide association mapping. Interestingly, seven QTL were co-migrating with N-related candidate genes. Four QTL were found to be significantly associated to increases of GPD, indicating that selecting for GPC could not affect final grain yield per spike. The combined approaches of candidate genes and genome-wide association mapping led to a better understanding of the genetic relationships between grain storage proteins and grain yield per spike, and provided useful information for marker-assisted selection programs.

Background Information on the nature and extent of genetic and genotype × environment (GE) interaction is extremely rare in wheat varieties under different sowing dates. In the present study, the GGE biplot method was conducted to investigate genotype × environment interaction effects and evaluate the adaptability and yield stability of 13 wheat varieties across eight sowing dates, in order to facilitate comparison among varieties and sowing dates and identify suitable varieties for the future breeding studies. Results Considerable genotypic variation was observed among genotypes for all of the evaluated traits, demonstrating that selection for these traits would be successful. Low broad sense heritability obtained for grain yield showed that, both genetic and non-genetic gene actions played a role in the control of this trait, and suggested that indirect selection based on its components which had high heritability and high correlation with yield, would be more effective to improve grain yield in this germplasm. Hence, selection based on an index may be more useful for improvement of this trait in recurrent selection programs. The results of the stability analysis showed that the environmental effect was a major source of variation, which captured 72.21% of total variation, whereas G and GE explained 6.94% and 18.33%, respectively. The partitioning of GGE through GGE biplot analysis showed that, the first two PCs accounted for 54.64% and 35.15% of the GGE sum of squares respectively, capturing a total of 89.79% variation. According to the GGE biplot, among the studied varieties, the performance of Gascogen was the least stable, whereas Sirvan, Roshan, and Pishtaz had superior performance under all sowing dates, suggesting that they have a broad adaptation to the diverse sowing dates. These varieties may be recommended for genetic improvement of wheat with a high degree of adaptation. Conclusion The results obtained in this study demonstrated the efficiency of the GGE biplot technique for selecting high yielding and stable varieties across sowing dates.

A key concern in agriculture is how to feed the expanding population and safeguard the environment from the ill effects of climate change. To feed a growing global population, food production and security are significant problems, as food output may need to double by 2050. Thus, more innovative and effective approaches for increasing agricultural productivity (hence, food production) are required to meet the rising demand for food. The world’s most widely cultivated grains include corn, wheat, and rice, which serve as the foundation for basic foods. This review focuses on some of the key most up-to-date approaches that boost wheat, rice, corn, barley, and oat yields with insight into how molecular technology and genetics may raise the production and resource-efficient use of these important grains. Although red light management and genetic manipulation show maximal grain yield enhancement, other covered strategies including bacterial-nutrient management, solar brightening, facing abiotic stress through innovative agricultural systems, fertilizer management, harmful gas emissions reduction, photosynthesis enhancement, stress tolerance, disease resistance, and varietal improvement also enhance grain production and increase plant resistance to harmful environmental circumstances. This study also discusses the potential challenges of the addressed approaches and possible future perspectives.

Background and aims Crop tolerance to waterlogging depends on factors such as species sensitivity and the stage of development that waterlogging occurs. The aim of this study was to identify the critical period for waterlogging on grain yield and its components, when applied during different stages of crop development in wheat and barley. Methods Two experiments were carried out (E1: early sowing date, under greenhouse; E2: late sowing date, under natural conditions). Waterlogging was imposed during 15-20 days in 5 consecutive periods during the crop cycle (from Leaf 1 emergence to maturity). Results The greatest yield penalties occurred when waterlogging was applied from Leaf 7 appearance on the main stem to anthesis (from 34 to 92 % of losses in wheat, and from 40 to 79 % in barley for E1 and E2 respectively). Waterlogging during grain filling reduced yield to a lesser degree. In wheat, reductions in grain number were mostly explained by reduced grain number per spike while in barley, by variations in the number of spikes per plant. Conclusions The time around anthesis was identified as the most susceptible period to waterlogging in wheat and barley. Exposing the crop to more stressful conditions, e.g. delaying sowing date, magnified the negative responses to waterlogging, although the most sensitive stage (around anthesis) remained unchanged.

HaHB4 is a sunflower transcription factor belonging to the homeodomain-leucine zipper I family whose ectopic expression in Arabidopsis triggers drought tolerance. The use of PCR to clone the HaHB4 coding sequence for wheat transformation caused unprogrammed mutations producing subtle differences in its activation ability in yeast. Transgenic wheat plants carrying a mutated version of HaHB4 were tested in 37 field experiments. A selected transgenic line yielded 6% more (P<0.001) and had 9.4% larger water use efficiency (P<0.02) than its control across the evaluated environments. Differences in grain yield between cultivars were explained by the 8% improvement in grain number per square meter (P<0.0001), and were more pronounced in stress (16% benefit) than in non-stress conditions (3% benefit), reaching a maximum of 97% in one of the driest environments. Increased grain number per square meter of transgenic plants was accompanied by positive trends in spikelet numbers per spike, tillers per plant, and fertile florets per plant. The gene transcripts associated with abiotic stress showed that HaHB4’s action was not dependent on the response triggered either by RD19 or by DREB1a, traditional candidates related to water deficit responses. HaHB4 enabled wheat to show some of the benefits of a species highly adapted to water scarcity, especially in marginal regions characterized by frequent droughts.

Background Abscisic acid (ABA) and sucrose act as molecular signals in response to abiotic stress. However, how their synergy regulates the source-sink relationship has rarely been studied. This study aimed to reveal the mechanism underlying the synergy between ABA and sucrose on assimilates allocation to improve grain yield and quality of rice. The early indica rice cultivar Zhefu802 was selected and planted in an artificial climate chamber at 32/24 °C (day/night) under natural sunlight conditions. Sucrose and ABA were exogenously sprayed (either alone or in combination) onto rice plants at flowering and 10 days after flowering. Results ABA plus sucrose significantly improved both the grain yield and quality of rice, which was mainly a result of the higher proportion of dry matter accumulation and non-structural carbohydrates in panicles. These results were mainly ascribed to the large improvement in sucrose transport in the sheath-stems in response to the ABA plus sucrose treatment. In this process, ABA plus sucrose significantly enhanced the contents of starch, gibberellic acids, and zeatin ribosides as well as the activities and gene expression of enzymes involved in starch synthesis in grains. Additionally, remarkable increases in trehalose content and expression levels of trehalose-6-phosphate synthase1 , trehalose-6-phosphate phosphatase7 , and sucrose non-fermenting related protein kinase 1A were also found in grains treated with ABA plus sucrose. Conclusion The synergy between ABA and sucrose increased grain yield and quality by improving the source-sink relationship through sucrose and trehalose metabolism in grains.

Increases in temperature have potentially influenced crop growth and reduced agricultural yields. Commonly, more fertilizers have been applied to improve grain yield. There is a need to optimize fertilizers, to reduce environmental pollution, and to increase agricultural production. Maize is the main crop in China, and its ample production is of vital importance to guarantee regional food security. In this study, the RGB and multispectral images, and maize grain yields were collected from an unmanned aerial vehicle (UAV) platform. To confirm the optimal indices, RGB-based vegetation indices and textural indices, multispectral-based vegetation indices, and crop height were independently applied to build linear regression relationships with maize grain yields. A stepwise regression model (SRM) was applied to select optimal indices. Three machine learning methods including: backpropagation network (BP), random forest (RF), and support vector machine (SVM) and the SRM were separately applied for predicting maize grain yields based on optimal indices. RF achieved the highest accuracy with a coefficient of determination of 0.963 and root mean square error of 0.489 (g/hundred-grain weight). Through the grey relation analysis, the N was the most correlated indicator, and the optimal ratio of fertilizers N/P/K was 2:1:1. Our research highlighted the integration of spectral, textural indices, and maize height for predicting maize grain yields.

Grain protein content (GPC) is one of the most important criteria to determine the quality of common wheat (Triticum aestivum). One of the major obstacles for bread wheat production is the negative correlation between GPC and grain yield (GY). Previous studies demonstrated that the deviation from this inverse relationship is highly heritable. However, little is known about the genetics controlling these deviations in common wheat. To fill this gap, we performed quantitative trait locus (QTL) analysis for GY, GPC, and four derived GY-GPC indices using an eight-way multiparent advanced generation intercross population comprising 394 lines. Interval mapping was conducted using phenotypic data from up to nine environments and genotypic data from a 20k single-nucleotide polymorphism array. The four indices were highly heritable (0.76–0.88) and showed distinct correlations to GY and GPC. Interval mapping revealed that GY, GPC, and GY-GPC indices were controlled by 6, 12, and 12 unique QTL, of which each explained only a small amount of phenotypic variance (R2 ≤ 10%). Ten of the 12 index QTL were independent of loci affecting GY and GPC. QTL regions harboured several candidate genes, including Rht-1, WAPO-A1, TaTEF-7A, and NRT2.6-7A. The study confirmed the usefulness of indices to mitigate the inverse GY-GPC relationship in breeding, though the selection method should reflect their polygenic inheritance.

Environmental and plant factors critical to the grain yields of bread (Triticum aestivum L.), durum (T. durum L.) and emmer (T. dicoccum L.) wheat cultivars were investigated at two Mediterranean rain-fed eld sites: Adana in southeastern Turkey (2009 and 2010) and Aleppo in northern Syria (2009). The grain yield (GY) and biological yield (BY) of most cultivars were higher in Adana than in Aleppo, and the lower GY in Aleppo resulted from lower harvest index (HI) and lower BY due to higher temperatures and lower rainfall. The variations in the HI among cultivars were greater in Adana than in Aleppo. The GY was closely related to the HI but not the BY across cultivars at each site, and a higher GY was accompanied by a superior conversion-eciency of incident radiation during the grain lling period for grain yield [GY/Ra, where Ra is the cumulative radiation for 30days after heading (D30)] across all observations. The GY/Ra correlated negatively with the average temperature for D30, and higher HI values resulted in higher GY/Ra. In Adana, the time from anthesis to physiological-maturity decreased as the average temperature for D30 increased, resulting in a lower HI. Cultivars exhibiting the early heading trait can eectively escape the negative impacts of terminal high-temperature and water-shortage conditions on the HI. The results suggested that the HI is a critical factor for GY across diverse wheat cultivars under terminal high-temperatures and water-shortages in Mediterranean areas, and the BY is also an important factor under severe water-limitation conditions