Pea Breeding Research Articles

Genomic and Transcriptomic Analysis of Pea (Pisum sativum L.) Breeding Line 'Triumph' with High Symbiotic Responsivity.

Pea (Pisum sativum L.), like most legumes, forms mutualistic symbioses with nodule bacteria and arbuscular mycorrhizal (AM) fungi. The positive effect of inoculation is partially determined by the plant genotype; thus, pea varieties with high and low symbiotic responsivity have been described, but the molecular genetic basis of this trait remains unknown. Here, we compare the symbiotically responsive breeding line 'Triumph' of grain pea with its parental cultivars 'Vendevil' (a donor of high symbiotic responsivity) and 'Classic' (a donor of agriculturally valuable traits) using genome and transcriptome sequencing. We show that 'Triumph' inherited one-fourth of its genome from 'Vendevil', including the genes related to AM and nodule formation, and reveal that under combined inoculation with nodule bacteria and AM fungi, 'Triumph' and 'Vendevil', in contrast to 'Classic', demonstrate similar up-regulation of the genes related to solute transport, hormonal regulation and flavonoid biosynthesis in their roots. We also identify the gene PsGLP2, whose expression pattern distinguishing 'Triumph' and 'Vendevil' from 'Classic' correlates with difference within the promoter region sequence, making it a promising marker for the symbiotic responsivity trait. The results of this study may be helpful for future molecular breeding programs aimed at creation of symbiotically responsive cultivars of pea.

Estimation of pea samples with different leaf types according to adaptability parameter

The purpose of the current study was to identify productive collection samples with high adaptive properties for use in pea breeding. The study was carried out at the Federal State Budgetary Scientific Institution “ARC “Donskoy”, located in the southern part of the Rostov region. The objects of study were 100 collection pea samples from the collection of the ARIGRP named after N. I. Vavilov, 47 samples of which were with a usual (leafy) and 53 samples with a leafless type of leaf. The variety ‘Aksaisky Usatiy 5’ was used as a standard. The years of study, differing in agroclimatic conditions during the vegetation period, made it possible to estimate collection samples according to adaptive properties. The mean productivity of the collection over the years of study was 2.10 t/ha (1.58–3.25 t/ha). Seed productivity in the group of samples with a leafless type (2.10 t/ha) was at the level with leafy samples (2.09 t/ha). At the same time, the coefficient of variation was lower in the group of leafless samples – 12.4 %, than in the group of leafy ones – 13.0 %. Seed productivity of the standard variety ‘Aksaisky Usatiy 5’ over the years of study was 2.00 t/ha, ranged from 1.42 to 3.30 t/ha. Among the leafy samples, the most productive ones were ‘OMK-3’ (2.46 t/ha), ‘221/73’ (2.55 t/ha), ‘269/80’ (2.67 t/ha), ‘525/80’ (2.68 t /ha), ‘Blagodatny’ (2.77 t/ha). Among the samples with a leafy type, the most productive ones turned out to be ‘Ramus’ (2.45 t/ha), ‘Svetozar’ (2.52 t/ha), ‘Lavr’ (2.55 t/ha), ‘Koral’ (2.56 t/ha), ‘Deviz’ (2.62 t/ha). The highest values of homeostaticity with high seed productivity were found in the samples with a usual leaf, such as ‘Blagodatny’ (4.14) and ‘525/80’ (5.23). And among the leafless samples, the best ones were ‘Deviz’ (7.57) and ‘Koral’ (7.60). The study results on the homeostaticity of seed productivity of pea samples were used in breeding work when selecting parental forms for hybridization.

Markers of resistance to pea aphid, Acyrthosiphon pisum Harris in Pisum sativum L. accessions

One of the major insect pests in Pisum sativum L. (is Acyrthosiphon pisum Harris (Hemiptera: pests in Pisum sativum L. (Hemiptera: Aphididae) is Acyrthosiphon pisum Harris (Hemiptera: Aphididae). An effective strategy for aphid control is the resistant host plant use. The current study aimed to identify resistance mechanisms and assess biochemical and morphological markers of pea aphid resistance in pea accessions. Meteorological variables affected the pea aphid density, which positively correlated with temperature, while precipitation amount and humidity negatively impacted. The aphid number was significantly and positively associated with the leaf area and the nitrogen content but negatively correlated with calcium and phosphorus levels. The pea aphid-resistant cultivars L 123-7-11, L 128-1and L 125-5 had small leaf areas, and high phosphorus and calcium content but a low nitrogen level. In the mutual influence of the plant indicators, phosphorus concentration had the highest negative impact on pea aphid density, followed by calcium. The plant marker inclusion in the pea breeding process is an efficient tool for a substantial selection program improvement for aphid resistance. Therefore, resistant host plants are essential tools promoting considerable selection program improvement for aphid resistance in the P. sativum breeding process and helping develop sustainable and environmentally friendly agriculture.

Genome-wide identification, expression analysis, and functional study of the bZIP transcription factor family and its response to hormone treatments in pea (Pisum sativum L.)

BackgroundBasic leucine zipper (bZIP) protein is a plant-specific transcription factor involved in various biological processes, including light signaling, seed maturation, flower development, cell elongation, seed accumulation protein, and abiotic and biological stress responses. However, little is known about the pea bZIP family.ResultsIn this study, we identified 87 bZIP genes in pea, named PsbZIP1 ~ PsbZIP87, via homology analysis using Arabidopsis. The genes were divided into 12 subfamilies and distributed unevenly in 7 pea chromosomes. PsbZIPs in the same subfamily contained similar intron/exon organization and motif composition. 1 tandem repeat event and 12 segmental duplication events regulated the expansion of the PsbZIP gene family. To better understand the evolution of the PsbZIP gene family, we conducted collinearity analysis using Arabidopsis thaliana, Oryza sativa Japonica, Fagopyrum tataricum, Solanum lycopersicum, Vitis vinifera, and Brachypodium distachyon as the related species of pea. In addition, interactions between PsbZIP proteins and promoters containing hormone- and stress-responsive cis-acting elements suggest that the regulation of PsbZIP expression was complex. We also evaluated the expression patterns of bZIP genes in different tissues and at different fruit development stages, all while subjecting them to five hormonal treatments.ConclusionThese results provide a deeper understanding of PsbZIP gene family evolution and resources for the molecular breeding of pea. The findings suggested that PsbZIP genes, specifically PSbZIP49, play key roles in the development of peas and their response to various hormones.

Yield Stability Analysis of Late Set Pigeon Pea (Cajanus cajan L.) Genotypes

Pigeon pea breeding program in Ethiopia has been started recently and is actively involved in improving the genetic yield potential to meet the needs of farmers in different parts of the country through genotype introduction. Since performance of the genotypes depends on the genetic potential of the crop and the environment in which the crop is grown, this study aimed at the evaluation of yield performance and stability of six late set pigeon pea genotypes including the standard check. Yield performances were evaluated at five locations namely Bako, Billo, Gute, Uke and Chewaka in parts of western Oromia during 2021 main growing seasons. The experiment in each location was arranged in a Randomized Complete Block Design with three replications. The results showed that the yield performances of late set pigeon pea genotypes were highly influenced by genotype-environment interaction (GEI). The yield components were significantly affected by GEI. The partitioning of the G + GE sum of squares showed that IPCA1 and IPCA2 were significant components which accounted for 29.72 % and 34.86 % of G + GE sum of squares, respectively. Highly significant mean square was observed for genotypes, genotypes by Environment interaction and environment indicating adaptation for high performance environments showing these genotypes were sensitive to environments and gave maximum yield when inputs are not limited. Genotypes ICEAP 01499 and ICEAP 01489 were stable and had relatively high yield performances across test environments. Hence, these two genotypes were identified as candidate genotypes to be verified for possible release in the subsequent season for Western Oromia and areas with similar agro-ecologies.

In vivo characterisation of field pea stem wall thickness using optical coherence tomography

BackgroundModern field pea breeding faces a significant challenge in selecting lines with strong stems that resist lodging. Traditional methods of assessing stem strength involve destructive mechanical tests on mature stems after natural senescence, such as measuring stem flexion, stem buckling or the thickness of dry stems when compressed, but these measurements may not correspond to the strength of stems in the living plant. Optical coherence tomography (OCT) can be used as a noncontact and nondestructive method to measure stem wall thickness in living plants by acquiring two- or three-dimensional images of living plant tissue.ResultsIn this proof-of-principle study, we demonstrated in vivo characterisation of stem wall thickness using OCT, with the measurement corrected for the refractive index of the stem tissue. This in vivo characterisation was achieved through real-time imaging of stems, with an acquisition rate of 13 milliseconds per two-dimensional, cross-sectional OCT image. We also acquired OCT images of excised stems and compared the accuracy of in vivo OCT measurements of stem wall thickness with ex vivo results for 10 plants each of two field pea cultivars, Dunwa and Kaspa. In vivo OCT measurements of stem wall thickness have an average percent error of − 3.1% when compared with ex vivo measurements. Additionally, we performed in vivo measurements of both stem wall thickness and stem width at various internode positions on the two cultivars. The results revealed that Dunwa had a uniform stem wall thickness across different internode positions, while Kaspa had a significantly negative slope of − 0.0198 mm/node. Both cultivars exhibited an increase in stem width along the internode positions; however, Dunwa had a rate of increase of 0.1844 mm/node, which is three times higher than that of Kaspa.ConclusionsOur study has demonstrated the efficacy of OCT for accurate measurement of the stem wall thickness of live field pea. Moreover, OCT shows that the trends of stem wall thickness and stem width along the internode positions are different for the two cultivars, Dunwa and Kaspa, potentially hinting at differences in their stem strength. This rapid, in vivo imaging method provides a useful tool for characterising physical traits critical in breeding cultivars that are resistant to lodging.

Advances in pea breeding and genomics: From traditional techniques to modern approaches

Peas, a highly valued annual legume vegetable with a rich history of domestication, are grown globally as a valuable exportoriented cash crop. Despite an increase in cultivation area and production, there has been only a slight improvement in green pea productivity, from 7.7 to 7.8 t/ha, over the last two decades. The primary focus for genetic improvement in peas is developing resistance to various biotic stressors, including diseases such as powdery mildew, downy mildew, rust, wilt, viral infections, and bacterial blight, as well as pests like leaf miners, aphids, pod borers, and pea stem flies. Traditional reeding approaches have played a significant role in the genetic improvement of peas, resulting in the development of veral cultivars in various segments; however, advanced breeding techniques such as marker-assisted selection, genomic selection, and genome editing hold great promise in enhancing genetic improvement by facilitating the identification and selection of desirable traits, such as resistance to biotic and abiotic stressors, improved yield, and increased nutrient content, through the introduction of precise genetic modifications. By targeting specific genomic regions associated with desired traits, these techniques can increase the efficiency and precision of breeding programs, ultimately leading to the development of more resilient and productive pea varieties.

Evaluation of forage quality in a pea breeding program using a hyperspectral sensing system

Forage quality evaluation is essential in breeding and selecting forage crops to improve livestock health and reduce greenhouse gas emissions. A non-destructive and high-throughput process to assess forage quality can advance cultivar development research and commercial feed analysis. The overall objective of this study was to estimate 12 biomass quality traits of field peas in the 2019 and 2020 field seasons using in-field hyperspectral spectroscopy (350–2500 nm) at the leaf level. Six machine-learning models were utilized to develop the relationships between each quality trait and four feature extraction datasets (first derivative reflectance data, vegetation indices, normalized difference spectral indices, and ratio spectral indices) of leaf reflectance spectra from the hyperspectral data. The high and consistent performance of all quality traits was produced from only ridge regression, elastic net regression, and random forest regression models in first derivative reflectance and vegetation indices datasets. In addition, higher prediction performance (0.81 < R2 < 0. 93; 0.05 < root mean square error (%) < 1.80; 0.03 < mean absolute error (%) < 1.32) was found in the random forest model using normalized difference spectral indices and ratio spectral indices datasets after utilizing a feature selection technique (leave one feature out). The results suggest that the proposed evaluation approach of spectroscopy may be applied to predict field pea quality traits in a timely fashion to assist breeders and farmers in their decision-making.

Five Regions of the Pea Genome Co-Control Partial Resistance to D. pinodes, Tolerance to Frost, and Some Architectural or Phenological Traits.

Evidence for reciprocal links between plant responses to biotic or abiotic stresses and architectural and developmental traits has been raised using approaches based on epidemiology, physiology, or genetics. Winter pea has been selected for years for many agronomic traits contributing to yield, taking into account architectural or phenological traits such as height or flowering date. It remains nevertheless particularly susceptible to biotic and abiotic stresses, among which Didymella pinodes and frost are leading examples. The purpose of this study was to identify and resize QTL localizations that control partial resistance to D. pinodes, tolerance to frost, and architectural or phenological traits on pea dense genetic maps, considering how QTL colocalizations may impact future winter pea breeding. QTL analysis revealed five metaQTLs distributed over three linkage groups contributing to both D. pinodes disease severity and frost tolerance. At these loci, the haplotypes of alleles increasing both partial resistance to D. pinodes and frost tolerance also delayed the flowering date, increased the number of branches, and/or decreased the stipule length. These results question both the underlying mechanisms of the joint control of biotic stress resistance, abiotic stress tolerance, and plant architecture and phenology and the methods of marker-assisted selection optimizing stress control and productivity in winter pea breeding.

Challenges in pea breeding for tolerance to drought: Status and prospects

AbstractDrought is increasingly frequent in the context of climate change and is considered a major constraint for crop yield. Water scarcity can impair growth, disturb plant water relations and reduce water use efficiency. Pea (Pisum sativum) is a temperate grain legume rich in protein, fibre, micronutrients and bioactive compounds that can benefit human health. In reducing pea yield because of drought, the intensity and duration of stress are critical. This review describes several drought resistance mechanisms in pea based on morphology, physiology and biochemical changes during/after the water deficit period. Drought tolerance of pea can be managed by adopting strategies such as screening, breeding and marker‐assisted selection. Therefore, various biotechnological approaches have led to the development of drought‐tolerant pea cultivars. Finally, the main objective of the current research is to point out some useful traits for drought tolerance in peas and also, mention the methods that can be useful for future studies and breeding programmes.

Assessment of genetic diversity in pigeon pea (Cajanus cajan) using micro satellite markers

An effective way to use germplasm for genetic improvement is to be aware of the genetic variation among crop genotypes. The objective of the present study was to assess the genetic diversity and population structure of 30 genotypes of pigeonpea from populations that were collected from various sources. In order to show a new structure within the pigeonpea genetic pool and to give crucial information for pigeonpea breeding operations, the predetermined study's goal was to define pigeonpea genotypes using a microsatellite marker technique. The genomic DNA of 30 pigeon pea genotypes were amplified with 20 SSR primers that produced 46 amplified bands, out of which 30 band were polymorphic (65.21%) and 16 bands were monomorphic (34.82%). Primer CcM 2977 generated a maximum number of amplified bands, of which 2 bands were polymorphic. Among 20 primers, only 8 primers showed the highest polymorphism (100%) and 5 primers were monomorphic in nature. Average of 2.30 bands per primer was amplified. The dendrogram constructed from the pooled data revealed six distinct clusters of which five were solitary. Cluster analysis of pigeon pea genotypes was based on Unweighted Pair Group Method with the Arithmetic average (UPGMA) method and the 30 pigeon pea genotypes were classified into six main groups. The present study indicated that the performance of SSR markers for the evaluation of genetic diversity could be beneficial for pigeon pea breeding. They could additionally be useful in genomic mapping research, developing pigeon pea cultivars with various genetics and reaping advanced crop productivity.

Selecting High-Performing and Stable Pea Genotypes in Multi-Environmental Trial (MET): Applying AMMI, GGE-Biplot, and BLUP Procedures.

A large amount of data on various traits is accumulated over the course of a breeding program and can be used to optimize various aspects of the crop improvement pipeline. We leveraged data from advanced yield trials (AYT) of three classes of peas (green, yellow, and winter peas) collected over ten years (2012-2021) to analyze and test key aspects fundamental to pea breeding. Six balanced datasets were used to test the predictive success of the BLUP and AMMI family models. Predictive assessment using cross-validation indicated that BLUP offered better predictive accuracy as compared to any AMMI family model. However, BLUP may not always identify the best genotype that performs well across environments. AMMI and GGE, two statistical tools used to exploit GE, could fill this gap and aid in understanding how genotypes perform across environments. AMMI's yield by environmental IPCA1, WAASB by yield plot, and GGE biplot were shown to be useful in identifying genotypes for specific or broad adaptability. When compared to the most favorable environment, we observed a yield reduction of 80-87% in the most unfavorable environment. The seed yield variability across environments was caused in part by weather variability. Hotter conditions in June and July as well as low precipitation in May and June affected seed yield negatively. In conclusion, the findings of this study are useful to breeders in the variety selection process and growers in pea production.

Predicting Dry Pea Maturity Using Machine Learning and Advanced Sensor Fusion with Unmanned Aerial Systems (UASs)

Maturity is an important trait in dry pea breeding programs, but the conventional process predominately used to measure this trait can be time-consuming, labor-intensive, and prone to errors. Therefore, a more efficient and accurate approach would be desirable to support dry pea breeding programs. This study presents a novel approach for measuring dry pea maturity using machine learning algorithms and unmanned aerial systems (UASs)-collected data. We evaluated the abilities of five machine learning algorithms (random forest, artificial neural network, support vector machine, K-nearest neighbor, and naïve Bayes) to accurately predict dry pea maturity on field plots. The machine learning algorithms considered a range of variables, including crop height metrics, narrow spectral bands, and 18 distinct color and spectral vegetation indices. Backward feature elimination was used to select the most important features by iteratively removing insignificant ones until the model’s predictive performance was optimized. The study’s findings reveal that the most effective approach for assessing dry pea maturity involved a combination of narrow spectral bands, red-edge, near-infrared (NIR), and RGB-based vegetation indices, along with image textural metrics and crop height metrics. The implementation of a random forest model further enhanced the accuracy of the results, exhibiting the highest level of accuracy with a 0.99 value for all three metrics precision, recall, and f1 scores. The sensitivity analysis revealed that spectral features outperformed structural features when predicting pea maturity. While multispectral cameras achieved the highest accuracy, the use of RGB cameras may still result in relatively high accuracy, making them a practical option for use in scenarios where cost is a limiting factor. In summary, this study demonstrated the effectiveness of coupling machine learning algorithms, UASs-borne LIDAR, and multispectral data to accurately assess maturity in peas.

Vegetable peas (Pisum sativum L.) diversity: An analysis of available elite germplasm resources with relevance to crop improvement

Aim of study: To determine the amount of diversity in pea breeding materials with the objective to classify a set of potential parents carrying novel/economic variations that could be used in future breed pea varieties. Area of study: ICAR–Indian Institute of Vegetable Research, Varanasi. Material and methods: A total of 45 pea accessions were analysed for phenotypic and molecular diversity using 17 agro-morphological traits and 52 SSR markers. Main results: All traits under investigation showed considerable genetic variation. The genotypes exhibited 6.7, 2.7 and 12-fold variation for traits viz., pods/plant, 10-pod weight and yield/plant, respectively. Among 52 SSR markers, 22 were found to be polymorphic. A total of 90 allelic variants were detected, with an average of 2.7 alleles/locus. PIC and D-values for markers AA135 (0.79 and 0.81) and PSMPSAD51 (0.7 and 0.74) were the highest, while AB40 (0.19 and 0.2) had the lowest. Two principal components PC1 and PC2 explained 46.96 and 23.96% of total variation, respectively. The clustering based on agro-morphological traits differentiated 45 individuals into three mega clusters, while SSR markers-based clustering classified these accessions into four groups. Research highlights: Based on their uniqueness, we identified a set of genotypes (VRPD-2, VRPD-3, PC-531, ‘Kashi Nandini’, ‘Kashi Udai’, ‘Kashi Mukti’, ‘Arkel’, VRPE-101, ‘Azad Pea-3’, EC865944, VRPM-901 and VRP-500) harbouring genes for various economic traits. The findings presented here will be extremely useful to breeders who are working on improvement of peas through selective introgression breeding.

Characterization of yellow pea (Pisum sativum L.) genotypes for performance (agronomic and quality) and stability across environments

AbstractBackground and ObjectivesYellow peas (Pisum sativum L.) are preferred for protein isolation. We characterized 21 yellow pea genotypes grown at Fairfield and Pullman, Washington in 2020 and 2021. To evaluate yellow pea genotypes for agronomic and quality traits across environments, data were collected on seed, flour, protein isolate, and starch‐rich byproducts.FindingsSeed yield and weight dropped by over 61% and 11%, respectively, in 2021 when compared to 2020. About 17%–20% of the flour was recovered as protein isolated with a protein recovery rate of 66%–69% and about 61%–64% was converted to starch‐rich byproduct. Heritability was fairly high for seed weight (TSW) and protein isolate yield but low for seed yield and the functional properties. Some genotypes combined merits for multiple traits based on the degree of relationship between those traits. It was possible to identify genotypes with favorable combinations of seed yield with either seed weight or flour protein as these two traits exhibited weak correlation with seed yield. Owing to a positive correlation between flour protein concentration and PIYLD, two genotypes combined the best of the two traits.Significance and NoveltyOverall, the findings of this study can be used to develop a tailored pea breeding program focused on plant‐based protein. As starch‐rich byproduct is the largest portion produced due to protein extraction, it would be beneficial to conduct more research on this component to facilitate product development.

Identification of SSR Markers Linked to Powdery Mildew Resistance in Table Pea (Pisum sativum var. Hortense L.)

Background: Powdery mildew is one of the major diseases of pea which leads to severe crop losses and also affects the quality of pods and seeds. Therefore, it is necessary to screen for resistant sources and introduce the resistant gene into cultivars. So, for marker-assisted pea breeding, rapid screening of pea germplasm for powdery mildew resistance is required. Methods: A total of twenty-eight pea lines were generated using half-diallel mating design with eight pea lines (AP-3, Kashi Nandini, Arkel, VL-7, PMR-53, Kashi Uday, PC-531and AP-1). These hybrids and parental lines were screened for powdery mildew resistance phenotypically by PDI and genotypically by SSR and SCAR markers. Result: Based on screening for powdery mildew resistance, the genotypes PMR-53, Kashi Nandini, Kashi Uday, VL-7, AP-1 and hybrids Kashi Nandini x VL-7, Kashi Nandini x PMR-53, Kashi Nandini x Kashi Uday, Kashi Nandini x AP-1, VL-7 x PMR-53, VL-7 x Kashi Uday, VL-7 x AP-1, PMR-53 x Kashi Uday, PMR-53 x AP-1and Kashi Uday x AP-1 were found relatively resistant to powdery mildew disease. Two SSR markers (AD237 and AD141) showed polymorphism between resistant and susceptible lines and can be used for genetic improvement of pea germplasm.

Evaluation of pea genotype PI180693 partial resistance towards aphanomyces root rot in commercial pea breeding.

The cultivation of vining pea (Pisum sativum) faces a major constraint with root rot diseases, caused by a complex of soil-borne pathogens including the oomycetes Aphanomyces euteiches and Phytophtora pisi. Disease resistant commercial varieties are lacking but the landrace PI180693 is used as a source of partial resistance in ongoing pea breeding programs. In this study, the level of resistance and their interaction with A. euteiches virulence levels of six new back-crossed pea breeding lines, deriving from the cross between the susceptible commercial cultivar Linnea and PI180693, were evaluated for their resistance towards aphanomyces root rot in growth chamber and green house tests. Resistance towards mixed infections by A. euteiches and P. pisi and commercial production traits were evaluated in field trials. In growth chamber trials, pathogen virulence levels had a significant effect on plant resistance, as resistance was more consistent against A. euteiches strains exhibiting high or intermediate virulence compared with lowly virulent strains. In fact, line Z1701-1 showed to be significantly more resistant than both parents when inoculated with a lowly virulent strain. In two separate field trials in 2020, all six breeding lines performed equally well as the resistant parent PI180693 at sites only containing A. euteiches, as there were no differences in disease index. In mixed infections, PI180693 exhibited significantly lower disease index scores than Linnea. However, breeding lines displayed higher disease index scores compared with PI180693, indicating higher susceptibility towards P. pisi. Data on seedling emergence from the same field trials suggested that PI180693 was particularly sensitive towards seed decay/damping off disease caused by P. pisi. Furthermore, the breeding lines performed equally well as Linnea in traits important for green pea production, again emphasizing the commercial potential. In summary, we show that the resistance from PI180693 interacts with virulence levels of the pathogen A. euteiches and is less effective towards root rot caused by P. pisi. Our results show the potential use of combining PI180693 partial resistance against aphanomyces root rot with commercially favorable breeding traits in commercial breeding programs.

Accuracy of Selection in Early Generations of Field Pea Breeding Increases by Exploiting the Information Contained in Correlated Traits.

Accuracy of predicted breeding values (PBV) for low heritability traits may be increased in early generations by exploiting the information available in correlated traits. We compared the accuracy of PBV for 10 correlated traits with low to medium narrow-sense heritability (h2) in a genetically diverse field pea (Pisum sativum L.) population after univariate or multivariate linear mixed model (MLMM) analysis with pedigree information. In the contra-season, we crossed and selfed S1 parent plants, and in the main season we evaluated spaced plants of S0 cross progeny and S2+ (S2 or higher) self progeny of parent plants for the 10 traits. Stem strength traits included stem buckling (SB) (h2 = 0.05), compressed stem thickness (CST) (h2 = 0.12), internode length (IL) (h2 = 0.61) and angle of the main stem above horizontal at first flower (EAngle) (h2 = 0.46). Significant genetic correlations of the additive effects occurred between SB and CST (0.61), IL and EAngle (-0.90) and IL and CST (-0.36). The average accuracy of PBVs in S0 progeny increased from 0.799 to 0.841 and in S2+ progeny increased from 0.835 to 0.875 in univariate vs MLMM, respectively. An optimized mating design was constructed with optimal contribution selection based on an index of PBV for the 10 traits, and predicted genetic gain in the next cycle ranged from 1.4% (SB), 5.0% (CST), 10.5% (EAngle) and -10.5% (IL), with low achieved parental coancestry of 0.12. MLMM improved the potential genetic gain in annual cycles of early generation selection in field pea by increasing the accuracy of PBV.

Estimation of Heterosis and the Combining Ability Effect for Yield and Its Attributes in Field Pea (Pisum sativum L.) Using PCA and GGE Biplots

Field pea (Pisum sativum L.) is a highly nutritious winter-season pulse crop. It is used as food, feed, and fodder and offers nutritional security to low-income people in developing countries. Different graphical approaches like Principal Component Analysis (PCA) and Genotype + Genotype × Environment (GGE) biplots were used along with the conventional line × tester to identify efficient parents, combining ability effects and distinct heterotic groups in field pea (Pisum sativum L.). The study used a line tester design (9 × 3) for seed yield and its associated traits. In the conventional analysis, lines Aman and HFP 715 and the tester GP02/1108, as well as crosses HFP 715 × GP02/1108, Aman × GP02/1108, and Pant P-243 × HFP 1426 showed the best GCA (General Combining Ability) and SCA (Specific Combining Ability) effects, respectively, for seed yield and its attributes. The σ2SCA &gt; σ2GCA, and σ2D &gt; σ2A in almost all the traits indicated control of non-additive gene effects. High manifestations of heterobeltiosis for seed yield were evidenced by the superiority of 24 out of 27 crosses over the better parent. The highest significant heterobeltiosis was observed in the cross HFP 715 × GP02/1108, followed by IPF 14-16 × GP02/1108, IPF 14-16 × HFP 1426, DDR-23 × HFP 1426, DDR-23 × GP02/1108, and Aman × GP02/1108 for yield and its attributes. The biplot techniques were used to analyze data and compare their results with conventional line × tester analysis. Overall, graphical analysis results were very similar to those of traditional analysis. Consequently, it can surely be assumed that these methods could be helpful in presenting data from field pea breeding experiments carried out with line × tester design.

Rust (Uromyces viciae-fabae Pers. de-Bary) of Pea (Pisum sativum L.): Present Status and Future Resistance Breeding Opportunities.

Uromyces viciae-fabae Pers. de-Bary is an important fungal pathogen causing rust in peas (Pisum sativum L.). It is reported in mild to severe forms from different parts of the world where the pea is grown. Host specificity has been indicated in this pathogen in the field but has not yet been established under controlled conditions. The uredinial states of U. viciae-fabae are infective under temperate and tropical conditions. Aeciospores are infective in the Indian subcontinent. The genetics of rust resistance was reported qualitatively. However, non-hypersensitive resistance responses and more recent studies emphasized the quantitative nature of pea rust resistance. Partial resistance/slow rusting had been described as a durable resistance in peas. Such resistance is of the pre-haustorial type and expressed as longer incubation and latent period, poor infection efficiency, a smaller number of aecial cups/pustules, and lower units of AUDPC (Area Under Disease Progress Curve). Screening techniques dealing with slow rusting should consider growth stages and environment, as both have a significant influence on the disease scores. Our knowledge about the genetics of rust resistance is increasing, and now molecular markers linked with gene/QTLs (Quantitative Trait Loci) of rust resistance have been identified in peas. The mapping efforts conducted in peas came out with some potent markers associated with rust resistance, but they must be validated under multi-location trails before use in the marker-assisted selection of rust resistance in pea breeding programs.