Selection Index Research Articles

Breeding potential of eggplant genotypes for mosaic virus disease tolerance using multivariate analysis

ABSTRACT Eggplant (Solanum melongena L.) cultivation is currently threatened in the tropics and subtropics, leading to enormous economic losses. Identifying genetically diverse donor parents for the development of mosaic-resistant varieties/hybrids in disease hotspot areas needs to be improved. Twenty-nine eggplant genotypes were screened for tolerance to mosaic disease and other economic traits to identify genotypes that can be utilized in future breeding. Based on field screening, the genotypes ‘2019/BRR Var-11,’ ‘2020/BRR Var-7,’ ‘2020/BRL Var-1,’ and ‘2020/BRL Var-5’ were found to be moderately resistant (>10%–20% disease severity) to mosaic disease, according to the disease rating scale. This study revealed no parallelism between genetic diversity and geographical divergence in eggplant. Multivariate analysis and the mean of economic characters indicate that the genotypes ‘2020/BRR Var-7,’ ‘2019/BRR Var-11,’ ‘2020/BRL VAR-1,’ ‘2020/BRL VAR-5,’ ‘2019/BRR Var-7,’ and ‘2019/BRL Var-7’ could be potential donors for mosaic disease resistance breeding. Phenylalanine ammonia-lyase, peroxidase, polyphenol oxidase, total phenols, and trichome density in the abaxial and adaxial leaves exhibited significant negative correlations with mosaic disease incidence, suggesting their application as selection indices for the identification of tolerant genotypes. Breeders might be able to introgress mosaic disease tolerance into commercial genotypes through systemic breeding to obtain prospective recombinants possessing desired economic characteristics.

The Effect of Preselection on the Level of Bias and Accuracy in a Broiler Breeder Population, a Simulation Study.

Many breeding programmes have to perform preselection, as genotyping and phenotyping all potential breeder candidates is often not a feasible option. There is need to understand how preselection affects the quality of the genomic estimated breeding values (EBVs) at final selection and thereby can affect genetic progress. This simulation study evaluated nine different preselection strategies in a broiler breeder programme and their effect on the quality of the (genomic) EBVs and genetic progress for three different traits: body weight (Body Weight), residual feed intake (RFI) and body weight gain (Gain). All birds have Body Weight recorded at preselection, but only the preselected birds were phenotyped for RFI and Gain and genotyped. The following criteria and intensities were studied: preselection based on phenotypic Body Weight (P), on a BLUP index (B) or on an ssGBLUP Index (G). Additionally, all criteria were studied with three different selection intensities, 10% of the males and 30% of the females (P10, B10, G10), 15% of the males and 45% of the females (P15, B15, G15) and 20% of the males and 60% of the females (P20, B20, G20). The accuracy at preselection with G10 was more accurate than B10 for both RFI and Gain (0.71 vs. 0.58 and 0.65 vs. 0.55 respectively), and also G15 was more accurate than B15 for both RFI and Gain (0.72 vs. 0.63 and 0.67 vs. 0.64 respectively); thus, the difference in accuracy reduces with an increasing number of birds being preselected. Differences in accuracy at final selection were mostly notable in the RFI trait between P10, B10 and G10, where G10 showed the highest accuracy (0.82 vs. 0.84 vs. 0.86 respectively). This difference in accuracy for RFI disappeared when more animals were preselected. For Body Weight and Gain, the BLUP preselection resulted in the highest accuracy at final selection when selection intensity decreased. The dispersion bias of EBVs at final selection was most pronounced in the P10 and P15 for Body Weight (0.81 and 0.92) but disappeared at P20 (0.97). The dispersion bias for all other criteria and traits was relatively small. Genetic progress was mostly affected when P10 or P15 was used at preselection, where the progress in Body Weight was noticeably higher, but prominently lower for RFI and Gain. The BLUP and ssGBLUP preselection had very similar genetic progress across traits and showed comparable improvements in the selection index. In conclusion, with high preselection intensity, the use of ssGBLUP at preselection might be favoured as there is an improvement in genetic progress across traits in all scenarios, which is due to the increased preselection accuracy. When preselection intensity decreases, the benefit of using ssGBLUP over BLUP at preselection disappears.

Genetic parameter estimation for traits related to the immune system against sheep red blood cells in Japanese quail

ABSTRACT 1. Improving resistance against disease is important in the animal and poultry industry. Besides drugs and vaccines, genetic selection for improved immune systems may be an effective approach. 2. Traits related to the immune system were studied in a 938 pedigreed Japanese quail population infected by sheep red blood cells (SRBC). Besides body weight at 35 d of age (BW35), weight of the gizzard (G), liver (Li), lungs (Lu), bursa (Bu), spleen (S), heart (H), and digestive track (D) and length of ileum (I) and caecum (C) were recorded. Total antibody (TA), mercaptoethanol-resistant (IgG), mercaptoethanol-sensitive titres (IgM), the number of monocyte (M), basophil (B) and eosinophil (E) and the ratio of heterophil to lymphocyte (H/L) were measured. Co-variance components were estimated via Gibbs sampling using GIBBS3F90 software. Five univariate animal models, including simple forms, were used for genetic parameter estimations, and the best model was determined by the deviance information criterion (DIC). Genetic and environmental correlations were estimated using a bivariate animal model. 3. Direct heritability estimates for internal organs ranged from 0.06 (Lu) to 0.57 (G) and for immune system traits from 0.05 (IgM) to 0.17 (IgG). Negative genetic correlations were found between BW35 and internal organs (−0.22 to −0.80). 4. Including one of the internal organs, such as the spleen, in the selection index improved the immune response in heavier birds. Additionally, because of the moderate heritability of IgG (0.17) and its effect on lasting immunity, selecting for higher IgG concentration may improve the resistance of Japanese quail against pathogens.

Breeding potential of red- and white-skinned onion (Allium cepa L.) genotypes for economic traits using multivariate analysis

ABSTRACT The improvement of onion (Allium cepa L.) depends on the characterization and evaluation of the germplasm to find superior genotypes with desired horticultural qualities and utilization of diversity in future breeding. The study aimed to evaluate advanced breeding lines (consisting of both red- and white-skinned types) through genetic component analyses and genetic divergence using multivariate analysis to identify genotypes that can be utilized in breeding for table and dehydration purposes, respectively. Genotypes ‘(EM-AVT-2) REC-21-48,’ ‘(RIET) RVA-21-01,’ and ‘(EM-AVT-2) REC-21-46’ were found to be high yielders. Higher estimates of the genotypic coefficient of variation, heritability and genetic advance for the percentage of the double bulb, total soluble solid, and reducing sugar contents indicate additive gene action, and selection based on these parameters would be more reliable. The plant height, polar diameter, equatorial diameter, and neck thickness emerged as important selection indices. Principal components such as the plant height, number of leaves per plant, percentage of bolting and double bulb had values of >1, accounting for 81.73% of the total variation. Based on mean values and multivariate analysis, onion genotypes ‘(EM-AVT-2) REC-21-46’ (red-skinned), ‘(EM-AVT-2) REC-21-52’ (red-skinned), ‘(WIET) WVA-21-47’ (white-skinned), ‘(WAVT-2) WVC-21-74’ (white-skinned), ‘(WIET) WVA-21-39’ (white-skinned) and ‘(WHTSS-AVT-2) WTC-21-84’ (white-skinned) could be utilized as potential genitors in future breeding for table and dehydration purpose.

Genetic Variation in Yield and Fruit Weight Among Strawberry (Fragaria ×ananassa) Cultivars and the Interaction With Year and Location Effect

ABSTRACTStrawberry (Fragaria ×ananassa) is an important crop in the United States. In this study, we analysed strawberry trials established across 7 years and two locations. Our objective was to estimate genetic parameters and predict breeding values for yield. Linear mixed models were used for unbalanced data across years and locations and study the genotype by year and location interactions. The results suggested significant differences between cultivars for fruit weight but not for total and marketable yield. Fruit weight was controlled by genetic factors, shown by the high heritability of cultivar means (0.93), whereas marketable yield and total yield showed lower heritability estimates (0.48 and 0.63, respectively), indicating a substantial environmental influence. For fruit weight, cultivars exhibited stability across years and locations, supported by the high additive genetic correlations observed for cultivar‐by‐year (r = 0.90) and cultivar‐by‐location interactions (r = 0.98). Fruit weight and total yield were genetically independent as suggested near zero additive genetic correlation (−0.06 ± 0.416) between two traits, and they should be considered for an index selection in the strawberry breeding strategy.

Grey preferences selection index with trimmed group preference for evaluating university dormitory renovation design

Renovating old buildings is a strategic approach to resource optimization and sustainability. However, dormitory design decisions are often made by a limited panel of experts, which risks excluding student preferences-key to the design process. Expert assumptions about student needs can lead to biased outcomes, and attempts to gather student input may suffer from respondent bias and inconsistent engagement, introducing data noise. This paper presents the Grey Trimmed Preferences Selection Index (GTPSI) Multi-Criteria Decision Making (MCDM) method, an improved preference selection index MCDM method that integrates expert weights and refines student feedback by filtering outliers, using interval grey numbers to address uncertainty. Applied to dormitory renovation proposals, GTPSI provides a stable ranking framework, as confirmed by whitening sensitivity analysis, ensuring consistency across grey bounds. To further validate the selected alternative, results are cross-verified with TOPSIS and ELECTRE-III, reinforcing GTPSI’s robustness in aligning renovation designs with actual student needs. The GTPSI method thus offers a comprehensive solution for integrating user-centered preferences into expert evaluations, promoting designs that better meet stakeholder expectations.

Selection of Atlantic Forest species in South Bahia, Brazil

The study aimed to evaluate the growth potential of several Atlantic Forest species in South Bahia, displayed in high dense grid and select the best trees through selection indexes. The experiment was carried out in the Comissão Executiva do Plano da Lavoura Cacaueira (CEPLAC) located in the South of Bahia state, Brazil. A total of 44 Atlantic Forest species were tested with three replications with plot containing 36 individuals that were arranged in 2 × 2 m spacing. Experimental parameters indicated high variability among species to high density spacing, which is favorable to commercial pure stands practiced in the forestry sector. Statistical selection of native species was evaluated using selection indices based on volumetric production and survival traits. These indices allowed select species by their adaptation into the experimental region. The top two ranked species were Pterygota brasiliensis Allemão (Folheiro ou pau-rei) and Plathymenia reticulata Benth. (Vinhático), which adapted well to a tropical, humid, and rainy weather, such as the climate in South Bahia, Brazil. From this study, it was possible to subsidize the selection of ideal species and individuals for seed collection, indication of materials to assemble breeding populations, cloning, and orientate future studies in South Bahia state, Brazil.

A multi-trait targeted genotype selection approach for screening drought tolerance in teosinte–maize hybrids

Drought tolerance is a complex characteristic and screening based on multiple traits rather than direct selection indices would result in more efficient selection. The study aims to select hybrids under optimal and water deficit conditions to achieve genetic gains in yield traits without compromising the other secondary traits viz., anthesis-silking interval, leaf chlorophyll content, delayed leaf rolling and leaf senescence, relative water content, and ROS scavenging using the multi-trait genotype ideotype distance index. The 30 teosinte-maize hybrids along with four checks were evaluated for two classes of traits: 16 morphometric and 14 physiological and biochemical traits, under well-watered (WW) and water-stress (WS) environments. Significant variations among the genotypes were observed for most traits and the presence of moderate to high heritability for most traits suggests direct selection for improvement of those traits. Significant correlation of the traits with yield and intercorrelations between traits indicate the advantage of indirect selection based on secondary traits. By assuming a selection intensity of 15% and equal weightage to all the traits, five genotypes were selected based on the MGIDI under each class of traits and in each environment. The genotypes viz. G19, G20, and G29 were commonly selected across both environments. The selection led to desired positive and negative selection gains for most of the traits studied and resulted in high positive gains for single plant yield of 35.6% under WW and 69.3% under WS. The strength and weakness plots effectively present the advantages and limitations of the selected genotypes under each environment. The multi-trait-based selection approach is an effective tool for selecting genotypes and designing breeding strategies for stress breeding.

Selection indexes in terms of functional features in modern dairy cattle breeding in Europe

Abstract The increase in demand for dairy products requires continued progress in dairy farming for a sustainable supply. Europe, known as the world's leading milk producer, plays a key role in meeting this growing demand. Modern dairy farming has moved beyond its historical focus on milk yield and now focuses on functional traits such as udder health, fertility and calf survival. As a result, selection indicators have become essential tools, combining multiple attributes to support selective decisions. However, these rates show considerable variability across countries, reflecting their distinct breeding goals. Poland's production and functionality (PF) Index emphasizes production and functional traits to enhance dairy cattle. Portugal uses the total economic merit (M€T) and total performance index (IPT) for a broader assessment covering a wider range of traits. Ukraine is transitioning towards a more comprehensive breeding system incorporating stress tolerance and longevity. Factors such as climate change and the need for sustainable practices drive this evolution, underscoring the economic importance of traits beyond mere production. Future trends may include features such as feed efficiency, methane emissions reduction and stress resistance. Diverse breeding objectives across countries lead to different selection index constructions, essential for effective selection, ranking and breeding of superior individuals. This comprehensive review offers insight into constantly evolving dairy farming strategies in Europe, with a focus on Poland, Portugal and Ukraine, while highlighting the key role of functional traits in shaping the future of dairy farming.

Assessing temporal variability in durum wheat performance and stability through multi-trait mean performance selection in Mediterranean climate

Durum wheat, a staple crop in Italy, faces substantial challenges due to increasing droughts and rising temperatures. This study examines the grain yield, agronomic traits, and quality of 41 durum wheat varieties over ten growing seasons in Southern Italy, utilizing a randomized complete block design. Notably, most varieties were not repeated between trials and 45% of the data was missing. The results indicate that the interaction between genotype and environment (GEI) significantly impacted all traits. High temperatures, elevated vapor pressure deficit (VPD), and water deficits severely affected yield and quality during warm years, while cooler years with favorable water availability promoted better growth and higher yields. Broad-sense heritability (H²) was generally low, suggesting that environmental factors played a major role in the observed traits. However, some traits, such as grain yield, ears per square meter, plant height, bleached wheat, thousand-grain weight, and hectoliter weight exhibited moderate to high heritability of the mean genotype (h²mg), indicating their potential for effective selection in breeding programs. Correlation analyses revealed strong connections between certain traits, such as protein content, and gluten index as well as between grain yield, and spike per square meter. Using the Multi-Trait Mean Performance Selection (MTMPS) index, the study identified six top-performing varieties. Among these, Antalis (G4) and Core (G18) consistently demonstrated strong adaptability and stability across different environments, particularly in hotter, drier conditions. Furio Camillo (G31) also exhibited valuable traits. This study highlights the challenges and complexities of breeding durum wheat for improved yield and quality in the face of climate change.

Using Multi-Sensor Data Fusion Techniques and Machine Learning Algorithms for Improving UAV-Based Yield Prediction of Oilseed Rape

Accurate and timely prediction of oilseed rape yield is crucial in precision agriculture and field remote sensing. We explored the feasibility and potential for predicting oilseed rape yield through the utilization of a UAV-based platform equipped with RGB and multispectral cameras. Genetic algorithm–partial least square was employed and evaluated for effective wavelength (EW) or vegetation index (VI) selection. Additionally, different machine learning algorithms, i.e., multiple linear regression (MLR), partial least squares regression (PLSR), least squares support vector machine (LS-SVM), back propagation neural network (BPNN), extreme learning machine (ELM), and radial basis function neural network (RBFNN), were developed and compared. With multi-source data fusion by combining vegetation indices (color and narrow-band VIs), robust prediction models of yield in oilseed rape were built. The performance of prediction models using the combination of VIs (RBFNN: Rpre = 0.8143, RMSEP = 171.9 kg/hm2) from multiple sensors manifested better results than those using only narrow-band VIs (BPNN: Rpre = 0.7655, RMSEP = 188.3 kg/hm2) from a multispectral camera. The best models for yield prediction were found by applying BPNN (Rpre = 0.8114, RMSEP = 172.6 kg/hm2) built from optimal EWs and ELM (Rpre = 0.8118, RMSEP = 170.9 kg/hm2) using optimal VIs. Taken together, the findings conclusively illustrate the potential of UAV-based RGB and multispectral images for the timely and non-invasive prediction of oilseed rape yield. This study also highlights that a lightweight UAV equipped with dual-image-frame snapshot cameras holds promise as a valuable tool for high-throughput plant phenotyping and advanced breeding programs within the realm of precision agriculture.

Revealing host genome–microbiome networks underlying feed efficiency in dairy cows

Ruminants have the ability to digest human-inedible plant materials, due to the symbiotic relationship with the rumen microbiota. Rumen microbes supply short chain fatty acids, amino acids, and vitamins to dairy cows that are used for maintenance, growth, and lactation functions. The main goal of this study was to investigate gene-microbiome networks underlying feed efficiency traits by integrating genotypic, microbial, and phenotypic data from lactating dairy cows. Data consisted of dry matter intake (DMI), net energy secreted in milk, and residual feed intake (RFI) records, SNP genotype, and 16S rRNA rumen microbial abundances from 448 mid-lactation Holstein cows. We first assessed marginal associations between genotypes and phenotypic and microbial traits through genomic scans, and then, in regions with multiple significant hits, we assessed gene-microbiome-phenotype networks using causal structural learning algorithms. We found significant regions co-localizing the rumen microbiome and feed efficiency traits. Interestingly, we found three types of network relationships: (1) the cow genome directly affects both rumen microbial abundances and feed efficiency traits; (2) the cow genome (Chr3: 116.5 Mb) indirectly affects RFI, mediated by the abundance of Syntrophococcus, Prevotella, and an unknown genus of Class Bacilli; and (3) the cow genome (Chr7: 52.8 Mb and Chr11: 6.1–6.2 Mb) affects the abundance of Rikenellaceae RC9 gut group mediated by DMI. Our findings shed light on how the host genome acts directly and indirectly on the rumen microbiome and feed efficiency traits and the potential benefits of the inclusion of specific microbes in selection indexes or as correlated traits in breeding programs. Overall, the multistep approach described here, combining whole-genome scans and causal network reconstruction, allows us to reveal the relationship between genome and microbiome underlying dairy cow feed efficiency.

Site selection for biomass-solar hybrid renewable energy facilities: Spatial modelling based on fuzzy logic-geographic information systems

The aim of this study is to propose a fuzzy logic-geographic information systems (GIS) based spatial model within the context of hybrid renewable energy, rural areas, and spatial planning approach in the site selection process of renewable energy facilities. In this context, potential suitability index and site selection suitability index for biomass-solar hybrid renewable energy facilities are determined within the spatial model, through a case study conducted in Türkiye. The study involves a two-stage process: the first stage focuses on establishing the national-level potential, and the second stage involves site selection in areas with high potential. Suitable locations are determined based on the optimal suitability index values, which are derived from fuzzy logic operators including AND, OR, SUM, PRODUCT, and GAMMA, applied in the GIS. The study results indicate that by assigning different γ values to the fuzzy GAMMA operator, results for the fuzzy AND (γ = 0.45–0.60), fuzzy OR (γ = 1.00), fuzzy SUM (γ = 1.00), and fuzzy PRODUCT (γ = 0.00–0.15) operators can be obtained. Furthermore, as the γ value increases, the suitability index also rises, and the optimal site selection occurs at γ = 0.90. The performance of the fuzzy GAMMA operator in big data sets and two-stage processes demonstrates its applicability in site selection.

Adding gastrointestinal parasite resistance to the breeding objective in hair sheep: initial steps.

The U.S. Maternal Hair index was designed by the National Sheep Improvement Program (NSIP) to increase total weight of lamb weaned per ewe lambing (TW). Producers are interested in adding gastrointestinal nematode (GIN) resistance to this breeding objective since parasitism causes substantial economic losses. The NSIP provides producers with estimated breeding values (EBV) for post-weaning fecal egg count (PFEC), an indicator of GIN resistance. Our objective was to gauge the effects of including PFEC as another selection criterion and goal trait. Selection index theory was used to construct 11 indexes. First was a benchmark index with TW as the goal. Weaning weight, and number of lambs born and weaned, were selection criteria. An index was then designed with PFEC added as a selection criterion. In 9 more indices, PFEC was also included as a goal trait, where the economic value of TW was scaled relative to that of PFEC. PFEC received a scaled economic value of -1 with that of TW increasing from +1 to +5 at +0.5 increments. Selection criteria were modelled as EBV or phenotypes. Annual genetic responses in goal traits were predicted. The top 3% of males and 26% of females were selected. Breeding values and phenotypes were simulated for 200 lambs by Cholesky decomposition and used to generate index scores, with 100 replicates run. Concordances with the animals selected in the benchmark scenario were determined. Using EBV as selection criteria, TW increased by 1.46kg/yr in the benchmark scenario. However, unfavorably, PFEC increased by 2.24%/yr. When using phenotypic criteria, TW increased by 0.52kg/yr and PFEC by 0.28%/yr. Once added as a goal trait, PFEC decreased regardless of the scaled economic value of TW. However, responses in TW were also reduced, although less so as its scaled economic value increased. A scaled economic value of +3 for TW appeared reasonable over other choices with 79% of the emphasis placed on TW in the breeding objective. With EBV as selection criteria, PFEC declined by 7.96%/yr with 98% of the response in TW retained. Also, on average, 64% of males and 80% of females were chosen in common with the benchmark. With phenotypic selection criteria, PFEC declined by 5.13%/yr and 94% of the response in TW was retained; 61% of males and 80% of females were chosen in common with the benchmark. Implementing an index with scaled economic values of +3 for TW and -1 for PFEC would balance gains in TW with reductions in PFEC.

Comparative analysis of ride comfort evaluation indices of high-speed vehicles based on a vehicle-seat-human body coupled dynamics model

As high-speed vehicles become increasingly lightweight, the influence of passengers on the vehicle vibration system cannot be overlooked. In this paper, a 3D vehicle-seat-human body coupled dynamics model is established to comprehensively evaluate the ride comfort of high-speed vehicles. The model combines the passenger biomechanics with the carbody flexibility. This is often neglected in previous studies and only the carbody vibrations are considered. Applying the validated model, four types of ride comfort evaluation indices are calculated and compared at 250–400 km/h. Results show that the ride comfort of high-speed vehicles at different speeds is excellent when the carbody acceleration and Sperling indices are used. While the results are significantly different when the total equivalent weighted acceleration and annoyance rate indices based on human body vibrations are used. At full load and speed of 400 km/h, the passengers sitting near the windows at both ends of the carbody felt ‘slightly uncomfortable’ or ‘less comfortable’ with an annoyance rate of about 30%, which indicates that up to 30% of the passengers might find the vibrations intolerable. This study underscores the necessity of integrating human body vibrations and passenger subjective psychology into ride comfort assessments, which can provide theory references for the selection and optimization of evaluation indices for high-speed lightweight vehicles.

Unsupervised selection of acoustic indices: An experimental comparison for characterizing unlabeled audio recordings from sub-Andean forest soundscapes

We present an experimental comparison of applying six unsupervised (i.e. not relying on class labels) and almost parameterless feature selection methods for ranking acoustic indices. The study is aimed at guiding the practitioner in choosing appropriate acoustic indices when, in absence of class labels, a small but meaningful number of features to characterize soundscapes is desired. Forty acoustic indices were considered, which correspond to seventeen temporal, spectral and soundscape features, along with their basic statistics. Three publicly available soundscape datasets, registered in a sub-Andean forest, were used for the experiments; moreover, several subsets were considered according to the different times of the day. Results reveal that the Acoustic Evenness Index is the most important one in terms of representational power according to the six considered selection criteria, followed by the Acoustic Complexity Index when conditions are relaxed to examine features ranked among the top-five. Besides, the Bioacoustic Index, the Acoustic Diversity Index and the Root Mean Square Energy stand out as important features when characterizing days in their separated parts.

Character association and path analysis for yield and its attributing traits among urdbean landraces

In the present study, character association and path analysis were studied among 23 urdbean genotypes for 11 quantitative traits. Correlation analysis divulged that seed yield/plant proclaimed a significant positive relationship between branches/plant, biological yield/plant, pods/plant, and pod length at both genotypic and phenotypic levels and also showed a direct effect on yield. Biological yield/plant, branches/plant and pods/plant had the most beneficial direct and indirect influences on the overall relationship between yield and other component attributes, indicating the high inter-dependent interaction among these traits. Therefore, these characteristics could be considered as optimum selection indices on a priority basis, which would be remarkable for improving genotype efficiency during genetic improvement.

Early Flowering and Maturity Promote the Successful Adaptation and High Yield of Quinoa (Chenopodium quinoa Willd.) in Temperate Regions

Quinoa (Chenopodium quinoa Willd.) can offer an alternative for staple food considering its tolerance to abiotic stresses and high seed quality. However, its cultivation in temperate regions has not been successful due to its photoperiod sensitivity and low seed yield. This study investigated the agronomical performance and quality traits of 48 accessions for cultivation in northern Europe. We conducted two-year field trials and phenotyped traits related to phenological development, plant architecture, yield components, seed quality, and disease resistance. The major determinants of seed yield in this study were days to flowering, days to maturity, thousand-kernel weight, and panicle density, while downy mildew susceptibility and stem lodging showed a negative correlation with seed yield. We developed a selection index to enable simultaneous selection based on different important agronomical traits. We evaluated the stability of different accessions over the two years of the experiment. Finally, we provided a list of 10 selected accessions that can be directly integrated and serve as new crossing parents in quinoa breeding programs for temperate regions.

Trait Association Studies to Determine Selection Indices in F5 Segregating Populations of Rice (Oryza sativa L.)

The experiment consisted of 38 promising F5 breeding lines developed and evaluated for correlation coefficient, path coefficient and selection indices studies with four check varieties viz., NVSR-2435, Sardar, Lalkada Gold and GR-18 in RBD with three replications during kharif-2023 at RRRS, Vyara, Gujarat. Genotypic correlation analysis revealed that grain yield per plant appeared to be positively and significantly correlated with productive tillers per plant, plant height, panicle length, straw yield per plant and 100 grain weight, which suggested that these characters can be improved simultaneously towards higher grain yield per plant by direct selection. Genotypic path analysis revealed that positive and direct effects on grain yield per plant were exhibited by days to maturity, productive tillers per plant, panicle length, 100 grain weight and straw yield per plant. Among these traits, panicle length, productive tillers per plant, 100 grain weight and straw yield per plant exhibited direct effect along with highly significant and positive association with grain yield per plant. Therefore, selection for such traits may be considered as most important yield attributing characters. The negligible residual effect of 0.037 was observed in path analysis, indicates that the model explained large proportion of variance. Thirty-one selection indices were constructed in all possible combinations of grain yield per plant (X1) along with four components viz., productive tiller per plant (X2), panicle length (X3), 100 grain weight (X4) and straw yield per plant (X5) through equal weight method. Among all the selection indices, the relative efficiency (%) and expected genetic advance (GA) was noted maximum with four characters selection index (I1245) compared to grain yield per plant (I1).

Genetic Studies on Yield and Its Components in Pigeonpea (Cajanus cajan L. Millsp.)

A field trial was carried out at the International Crop Research Institute for the Semi-Arid Tropics, Patancheru, Hyderabad during Kharif, 2023-24, with the aim of studying genetic parameters for yield and its components among pigeonpea genotypes which provides in developing an apt selection index to be included in the breeding programs. This research was conducted on 200 genotypes of Indian pigeon pea on seven traits. Data were analysed to calculate analysis of variance (ANOVA), phenotypic coefficient of variation (PCV), genetic coefficient of variation (GCV), broad sense heritability (h2bs) and estimated genetic progression as percentage mean using R software v 4.3.1. The results of the analysis of variance showed that there was a significant difference for days to 50% flowering, plant height (cm), hundred seed weight and seed yield per plant among the genotypes. The genotypic coefficient of variance was lower than the genotypic coefficient of variance for all the traits under selection. Higher extent of genotypic coefficient of variation is manifested by days to 50% flowering, plant height, seeds per pod, hundred seed weight and seed yield per plant. High heritability coupled with genetic advance as per cent mean was observed in seed yield per plant, number of days to 50% flowering and plant height. The results concluded that seed yield per plant, number of days to flowering up to 50%, and plant height are the traits that should be emphasized as selection indicator in Indian pigeon pea breeding programs.