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Alfalfa (Medicago sativa L.) is the most widely grown forage legume crop worldwide. Yield and plant height are important agronomic traits influenced by genetic and environmental factors. The objective of this study was to identify quantitative trait loci (QTL) and molecular markers associated with alfalfa yield and plant height. To understand the g...
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Biomass yield and Feed Quality are the most important traits in alfalfa (Medicago sativa L.), which directly affect its economic value. Drought stress is one of the main limiting factors affecting alfalfa production worldwide. However, the genetic and especially the molecular mechanisms for drought tolerance in alfalfa are poorly understood. In thi...
Background
Alfalfa ( Medicago sativa L.) is one of the most important forage crops, but its seed productivity varies from year to year due to weather conditions. Alfalfa is a forage crop rich in nutrients which makes it valuable. The present 5-year study aimed to identify the promising alfalfa varieties based on their economic and biological charac...
Alfalfa is the most important forage legume in the production of voluminous fodder. Although not primarily produced for its seeds, the seed yield is still important for the recognition and commercial viability of the cultivars on the market. Creating a cultivar of superior yield and forage quality with satisfactory seed production is one of the big...
On the Ground
•The exotic invasive plant Parthenium hysterophorus is invading rangelands in Africa while causing negative effects on the biodiversity, environment, economy, and human and animal health because eco-friendly control methods are lacking.
•We conducted experiments to investigate the suppressive effects of forage legume plant species; D...
Fodder galega (Galega orientalis Lam.) is a perennial forage legume that can be an alternative to alfalfa (Medicago sativa L.) under more difficult environmental conditions. Galega is characterized by leafiness, and technology is now available to harvest leaves separately from stems, providing opportunity to utilize the more nutritious leaves as a...
Citations
... In order to address this issue, Chinese alfalfa breeders are actively working towards developing domestic cultivars that demonstrate exceptional productivity and adaptability across various ecological regions. PH serves as a key phenotypic trait for artificial selection in breeding programs, as it exhibits a significant positive correlation with forage yield [19]. Increasing PH can effectively improve biomass yield. ...
... PH is a key agronomic trait influenced by a complex genetic network. Although some QTLs and SNPs associated with PH have been reported [19,20,22,24,25], the genetic basis for the variation in PH in alfalfa remains largely unknown. In this study, the PHs of 220 diverse alfalfa accessions were measured of different origins and improved statuses in five distinct environments. ...
... As a result, considerable efforts have been devoted to uncovering the genetic basis of PH in this species. Previous studies have identified markers that are significantly associated with PH in specific genetic backgrounds using biparental [19,24,35] or natural association mapping populations [20,22,25]. However, few QTLs or SNPs have been validated for different genetic backgrounds. ...
Background
Plant height (PH) is an important agronomic trait influenced by a complex genetic network. However, the genetic basis for the variation in PH in Medicago sativa remains largely unknown. In this study, a comprehensive genome-wide association analysis was performed to identify genomic regions associated with PH using a diverse panel of 220 accessions of M. sativa worldwide.
Results
Our study identified eight novel single nucleotide polymorphisms (SNPs) significantly associated with PH evaluated in five environments, explaining 8.59–12.27% of the phenotypic variance. Among these SNPs, the favorable genotype of chr6__31716285 had a low frequency of 16.4%. Msa0882400, located proximal to this SNP, was annotated as phosphate transporter 3;1, and its role in regulating alfalfa PH was supported by transcriptome and candidate gene association analysis. In addition, 21 candidate genes were annotated within the associated regions that are involved in various biological processes related to plant growth and development.
Conclusions
Our findings provide new molecular markers for marker-assisted selection in M. sativa breeding programs. Furthermore, this study enhances our understanding of the underlying genetic and molecular mechanisms governing PH variations in M. sativa.
... Agronomic traits of plants dynamically reflect their growth and development processes, which are closely linked to crop yield He et al., 2020). The NPI treatments increased the radish plant height, maximum leaf length and maximum leaf width as compared to CI under similar SWC conditions ( Figure 2). ...
Compared to fluctuating soil water (FW) conditions, stable soil water (SW) can increase plant water use efficiency (WUE) and improve crop growth and aboveground yield. It is unknown, however, how stable and fluctuating soil water affect root vegetables. Here, the effects of SW and FW were studied on cherry radish in a pot experiment, using negative pressure irrigation and conventional irrigation, respectively. The assessed effects included agronomic parameters, physiological indices, yield, quality and WUE of cherry radish. Results showed that under similarly average soil water contents, compared with FW, SW increased plant photosynthetic rate, stomatal conductance and transpiration rate, decreased leaf proline content by 13.7–73.3% and malondialdehyde content by 12.5–40.0%, and increased soluble sugars content by 6.3–22.1%. Cherry radish had greater biomass accumulation and nutrient uptake in SW than in FW. Indeed, SW increased radish output by 34.6–94.1% with no influence on root/shoot ratio or root quality. In conclusion, soil water stability affected directly the water physiological indicators of cherry radish and indirectly its agronomic attributes and nutrient uptake, which in turn influenced the crop biomass and yield, as well as WUE. This study provides a new perspective for improving agronomy of root crops and WUE through managing soil water stability.
... This difference may be caused by variations in the assumptions of SNP effects and variance of Bayesian methods (de los Campos et al., 2009;Erbe et al., 2012;Habier et al., 2011;Meuwissen et al., 2001) and renders them less suitable for complex quantitative traits when compared to rrBLUP (Beaulieu et al., 2014;Meuwissen et al., 2001). All the agronomic traits examined in our research are quantitative traits and are governed by a combination of multiple large-effect and numerous small-effect genes (Adhikari et al., 2019;He et al., 2020;Jiang et al., 2022;Li, Alarcón-Zúñiga, et al., 2015;Lin et al., 2021;Mackay, 2001;McCord et al., 2014;Wang et al., 2020;Zhang et al., 2022), and the rrBLUP method may offer better predictions for these traits compared to the Bayesian methods. Although rrBLUP method may seem superior, its prediction accuracy is not consistently better than that of Bayesian methods. ...
Effects of individual single‐nucleotide polymorphism (SNP) markers and the size of “training” and “test” populations affect prediction accuracy in genomic selection (GS). This study evaluated 11 subsets of 4932 SNPs using six genetic additive methods to understand marker density in GS prediction in alfalfa (Medicago sativa L.). In the GS methods, the effect of “training” to “test” population size was also evaluated. Fourteen alfalfa populations sampled from long‐term grazing sites were genotyped using genotyping by sequencing for the identification of SNPs. These populations were also phenotyped for six agromorphological and three nutritive traits from 2018 to 2020. The accuracy of GS prediction improved across six GS methods when the ratio of “training” to “test” population size increased. However, the prediction accuracy of the six GS methods reduced to a range of −0.27 to 0.11 when random, uninformative SNPs were used. In this study, five Bayesian methods and ridge‐regression best linear unbiased prediction (rrBLUP) method had similar GS accuracies for “training” sets, but rrBLUP tended to outperform Bayesian methods in independent “test” sets when SNP subsets with high mean‐squared‐estimated‐marker effect were used. These findings can enhance the application of GS in alfalfa genetic improvement.
... Agronomic traits such as RSL, ST, FW, TFW, PH, NS, RDF, DW, and TDW are key indicators used to evaluate both the quality (Bhattarai et al., 2020;Jia et al., 2022;Sayed et al., 2022) and productivity of alfalfa varieties (Singer et al., 2017). However, previous studies on alfalfa have primarily focused on agronomic traits in a single place or year (Tucak et al., 2008;Inostroza et al., 2021) as well as the development of simple sequence repeat primers (Flajoulot et al., 2005;He et al., 2020) and transcriptome analysis (Chen et al., 2011;Liu et al., 2017). ...
Alfalfa ( Medicago sativa L.), an important perennial legume forage crop with high nutritional value and forage yield, is widely used in animal husbandry. However, it is very sensitive to aluminum, which severely limits its growth in acidic soils. In this study, we analyzed the genotype variation of each agronomic trait in 44 alfalfa varieties in two acidic soil environments. Then, analysis of variance (ANOVA) of the variance components was performed using the Residual Maximum Likelihood (REML). The best linear unbiased predictor analysis was used to obtain the mean trait of each variety, and the mean values were used to construct the mean matrix of varieties × traits and interaction analysis of varieties × years. The results showed that there was significant ( P < 0.05) genotypic variation for each trait of the 44 varieties and the genetic diversity was abundant. The average repeatability ( R value) of interannual plant height (PH), stem thickness (ST), number of branches (NS), fresh weight (FW), total fresh weight (TFW), and total dry weight (TDW) was high (0.21–0.34), whereas the genetics were relatively stable. PH, NS, FW, TFW, and dry weight (DW) were positively correlated ( P < 0.01) with TDW. Six alfalfa varieties (Algonquin, Xinjiang daye, Trifecta, Vernal, WL354HQ, and Boja) with excellent TDW and TFW were identified in different years, environmental regions, and climatic altitudes. Our research results can provide suggestions and critical information regarding the future improvement and development of new alfalfa strains and varieties that are resistant to acidic soil conditions.
... The height of alfalfa could positively correlate with the yield (Lyons et al., 2016) and even the forage quality (Owens et al., 1995) and therefore, it has been used to estimate yield or forage quality characteristics by many researchers (Ventroni et al., 2010;Noland et al., 2018). Height is mainly characterized by genetic factors (He et al., 2020) but environmental factors might have a substantial effect on the height of alfalfa . Besides, Cui et al. (2021) determined that the height of alfalfa decreased with the advancing years of the plantation. ...
The frequency of irrigation and deficit irrigation management are drawing attention because water resources are becoming limited year by year, especially in the last decade. Besides, the rate and application time of P fertilization gained more importance just after the researchers found out it is more effective than N-fertilizing for alfalfa cultivation. This study aimed to determine the effects of different irrigation managements (seasonal deficit, intervals of 5, 7, and 9 days), phosphorus application season (autumn and spring), and the rate of P fertilizer (0, 30, 60, 90 kg ha-1) on the yield and forage quality of alfalfa. The experiment was conducted in the 2019-2020 years, which was the 3rd and the 4th years of alfalfa respectively, under semi-arid Mediterranean conditions. The stand height and fiber content (NDF) were higher in the autumn application. However, forage contained more nitrogen in the spring application. A higher amount of water (800 mm) with higher irrigation frequency (5 days – I5d) caused a reduction in yield due to water excess. The yield was the highest (21.34 t ha-1) and the forage quality was better in 448 mm water application with 9 days intervals (I9d). Seasonal deficit water management caused a significant loss in yield and quality. Nevertheless, 18.04 t ha-1 dry matter yield with 24.05 % CP content was recorded at seasonal deficit water management. P fertilization increased the yield and forage quality. The yield was the highest (20.23 t ha-1) at the rate of 90 kg ha-1 P fertilizer, but yield and forage quality characteristics were similar between 30, 60, and 90 kg ha-1 P. The results showed that P fertilization could be done in both autumn and spring at the rate of 30 kg ha-1 and 448 mm water could be applied at 9 days intervals for fulfilling performance under semi-arid Mediterranean conditions. When water resources are very scarce, the seasonal water deficit should be applied, especially in late summer.
... In the United States, alfalfa production reaches 60 Mt as hay, silage, and pasture/cover crop, making alfalfa the third most valuable commodity in the country (USDA-NASS, 2017). In addition to its economic value, alfalfa can promote sustainable cropping systems due to its ability to fix nitrogen and contribute to nutrient cycling and is a highquality forage for livestock (Annicchiarico & Pecetti, 2021;Fernandez et al., 2019;He et al., 2020;Pilorgé & Muel, 2016). Alfalfa is allogamous and autotetraploid (2n = 4x = 32), its cultivars are synthetic populations consisting of highly variable and heterozygous plants (Annicchiarico & Pecetti, 2021), it is pollinated by various bee species, and it is characterized by severe inbreeding depression (Li & Brummer, 2009). ...
... Various traits are considered crucial in an alfalfa breeding: dry matter yield (DMY), forage nutritive value, canopy height (CH), fall dormancy, persistence, and resistance to biotic and abiotic stresses (Acharya et al., 2020;Hawkins & Yu, 2018;He et al., 2020). Fall dormancy is a key trait for alfalfa adaptation to different environmental conditions (Ventroni et al., 2010). ...
... The PA varied among harvests for both traits on both CV schemes. These differences in PA among the different harvests can be associated with environmental variation among harvests because alfalfa growth and development are influenced by interactions (GxE) (Acharya et al., 2020;Annicchiarico et al., 2016;He et al., 2020). The PA also depends on the trait heritability (Xu et al., 2020). ...
Genomic selection (GS) has proven to be an effective method to increase genetic gain rates and accelerate breeding cycles in many crop species. However, its implementation requires large investments to phenotype of the training population and for routine genotyping. Alfalfa (Medicago sativa L.) is one of the major cultivated forage legumes, showing high‐quality nutritional value. Alfalfa breeding is usually carried out by phenotypic recurrent selection and is commonly done at the family level. The application of GS in alfalfa could be simplified and less costly by genotyping and phenotyping families in bulks. For this study, an alfalfa reference population composed of 142 full‐sib and 35 half‐sib families was bulk‐genotyped using target enrichment sequencing and phenotyped for dry matter yield (DMY) and canopy height (CH) in Florida, USA. Genotyping of the family bulks with 17,707 targeted probes resulted in 114,945 single‐nucleotide polymorphisms. The markers revealed a population structure that matched the mating design, and the linkage disequilibrium slowly decayed in this breeding population. After exploring multiple prediction scenarios, a strategy was proposed including data from multiple harvests and accounting for the G×E in the training population, which led to a higher predictive ability of up to 38 and 24% for DMY and CH, respectively. Although this study focused on the implementation of GS in alfalfa families, the bulk methodology and the prediction schemes used herein could guide future studies in alfalfa and other crops bred in bulks.
... In recent years, high density linkage maps have been constructed for alfalfa (Adhikari et al. 2018;Zhang et al. 2019Zhang et al. , 2020. Some QTLs related to main traits including yield Zhang et al. 2019;He et al. 2020), flowing time Zhang et al. 2020), fall dormancy and winter-hardiness (Adhikari et al. 2018) and leaf rust resistance , have been mapped in the tetraploid genome. However, there is scarce information about QTLs associated with spring regrowth. ...
Spring regrowth is an important trait for perennial plants including alfalfa, the most cultivated forage legume worldwide. However, the genetic and genomic basis of the trait is largely unknown in alfalfa due to its complex genetic background of the tetroploid genome. The objective of this study was to identify quantitative trait loci (QTLs) associated with spring regrowth using high-resolution genetic linkage maps we constructed previously. In total, 36 significant additive effect QTLs for the trait were detected. Among them, 10 QTLs individually explained more than 10% of the phenotypic variation (PVE) with four in P1 and six in P2. Six overlapped QTLs intervals were detected with two and four intervals distributed in P1 and P2, respectively. In P1, both overlapped genomic regions were located on homolog 7D. In P2, the four QTLs with PVE>10% were co-localized on homolog 6D. Meanwhile, six pairs of significant epistatic QTLs were identified in P2. Screening of potential candidate genes associated with four overlapped QTLs (qCP2019-8, qLF2019-5, qLF2020-4, and qBLUP-3) narrowed down one candidate annotated as MAIL1. The Arabidopsis homolog gene has been reported to play an important role in plant growth. Therefore, the detected QTLs are valuable resources for genetic improvement of alfalfa spring vigor using marker-assisted selection (MAS), and further identification of the associated genes would provide insights into genetic control of spring regrowth in alfalfa.
... Nevertheless, HA has become a target breeding trait among alfalfa breeders more recently (Sakiroglu and Brummer, 2017;Dos Santos et al., 2018;Adhikari et al., 2019;Acharya et al., 2020;Benabderrahim et al., 2020;He et al., 2020;Ren et al., 2021;Tang et al., 2021). However, traditional field phenotyping for HA is based on the destructive sampling of experimental units at the ground level, weighing fresh samples, drying, and weighing dried samples to estimate dry matter content. ...
The application of remote sensing in plant breeding is becoming a routine method for fast and non-destructive high-throughput phenotyping (HTP) using unmanned aerial vehicles (UAVs) equipped with sensors. Alfalfa (Medicago sativa L.) is a perennial forage legume grown in more than 30 million hectares worldwide. Breeding alfalfa for herbage accumulation (HA) requires frequent and multiple phenotyping efforts, which is laborious and costly. The objective of this study was to assess the efficiency of UAV-based imagery and spatial analysis in the selection of alfalfa for HA. The alfalfa breeding population was composed of 145 full-sib and 34 half-sib families, and the experimental design was a row-column with augmented representation of controls. The experiment was established in November 2017, and HA was harvested four times between August 2018 and January 2019. A UAV equipped with a multispectral camera was used for HTP before each harvest. Four vegetation indices (VIs) were calculated from the UAV-based images: NDVI, NDRE, GNDVI, and GRVI. All VIs showed a high correlation with HA, and VIs predicted HA with moderate accuracy. HA and NDVI were used for further analyses to calculate the genetic parameters using linear mixed models. The spatial analysis had a significant effect in both dimensions (rows and columns) for HA and NDVI, resulting in improvements in the estimation of genetic parameters. Univariate models for NDVI and HA, and bivariate models, were fit to predict family performance for scenarios with various levels of HA data (simulated in silico by assigning missing values to full dataset). The bivariate models provided higher correlation among predicted values, higher coincidence for selection, and higher genetic gain even for scenarios with only 30% of HA data. Hence, HTP is a reliable and efficient method to aid alfalfa phenotyping to improve HA. Additionally, the use of spatial analysis can also improve the accuracy of selection in breeding trials.
... In support of the symposium, The Crop Journal arranged a special issue with the title "Quantitative genetics in the omics era". After peer review, 17 articles were finally selected, including one review article on genomic selection [12], five articles on analytical methods and tools for quantitative traits [13][14][15][16][17], six articles on genetic studies of quantitative traits [18][19][20][21][22][23], and five articles on applications in breeding for quantitative traits [24][25][26][27][28]. ...
... Three articles in this special issue address theoretical aspects of QTL mapping: ordering of high-density molecular markers in linkage map construction [13], improvement of time efficiency in GWAS using high-density SNP markers [15], and statistical methods suitable for multi-trait GWAS [16]. Several articles address the application of QTL mapping for kernel shape and color in durum wheat [18], panicle traits in rice [19], seed flooding tolerance in soybean [20], branch number in soybean [21], seed oil content in soybean [22], yield and plant height in alfalfa [23], and seed glucosinolate content in Brassica napus [27]. In the study of Sobhi et al. [21], one major-effect QTL was further localized to a chromosomal region 116 kb in length and a candidate gene in this region was confirmed to control branch number in soybean. ...
Late embryonic development abundant proteins (LEAs) are a large family of proteins commonly existing in plants. LEA_2 is the largest subfamily in the LEA, it plays an important role in plant resistance to abiotic stress. In order to explore the characteristics of LEA_2 gene family members in alfalfa (Medicago sativa L.), 155 members of LEA_2 (MsLEA_2) family were identified from alfalfa genome. Bioinformatics analysis was conducted from the aspects of phylogenetic relationship, chromosome distribution, chromosome colinearity, physical and chemical properties, motif composition, exon-intron structure, cis-element and so on. Expression profiles of MsLEA_2 gene were obtained based on Real-time fluorescent quantitative PCR (qRT-PCR) analysis and previous RNA-seq data under aluminum (Al) stress. Bioinformatics results were shown that the MsLEA_2 genes are distributed on all 32 chromosomes. Among them, 85 genes were present in the gene clusters, accounting for 54.83%, and chromosome Chr7.3 carries the largest number of MsLEA_2 (19 LEA_2 genes on Chr7.3). Chr7.3 has a unique structure of MsLEA_2 distribution, which reveals a possible special role of Chr7.3 in ensuring the function of MsLEA_2. Transcriptional structure analysis revealed that the number of exons in each gene varies from 1 to 3, and introns varies from 0 to 2. Cis-element analysis identified that the promoter region of MsLEA_2 is rich in ABRE, MBS, LTR, and MeJARE, indicating MsLEA_2 has stress resistance potential under abiotic stress. RNA-seq data and qRT-PCR analyses showed that most of the MsLEA_2 members were up-regulated when alfalfa exposed to Al stress. This study revealed that phylogenetic relationship and possible function of LEA_ 2 gene in alfalfa, which were helpful for the functional analysis of LEA_ 2 proteins in the future and provided a new theoretical basis for improving Al tolerance of alfalfa.
