Variation In Grain Yield Research Articles

Growth, productivity, quality and profitability of organic finger millet under cultivar and nutrient management options

Cultivars and nutrient management determine productivity and quality of the organic finger millet. A field trial was carried out during the kharif season 2022 at Binjhagiri, Bhubaneswar, Odisha comprising the four cultivars of finger millet and four nutrient management practices through organic manure combinations with split plot design replicated thrice. The four cultivars namely, C1- ‘Arjuna’, C2- ‘Bhairabi’, C3- ‘Chilika’ and C4- ‘Kalua’ were assigned to the main plots. Recommended dose of nitrogen (RDN) such as 50 kg ha-1 was supplied through varying percentage of organic manures namely, farmyard manure (FYM), neem oilcake (NOC) and vermicompost (VC). Four nutrient management practices such as N1- FYM (RDN100), N2- FYM (RDN40) + NOC (RDN30) + VC (RDN30), N3- FYM (RDN50) + NOC (RDN25) + VC (RDN25), N4- FYM (RDN60) + NOC (RDN20) + VC (RDN20) were assigned to the sub-plots. Combination of the treatments viz., C3N2, C4N2, C3N3 and C4N3 excelled over the others for grain and protein yields and profitability, but treatment combination C4N2 was the most promising considering the parameters like grain yield (2.10 tha-1), net return (Rs. 36940 ha-1), protein yield (165.75 kg ha-1), calcium yield (7.34 kg ha-1) and iron yield (0.137 kg ha-1). There was significant positive correlation of grain yield with all growth-yield parameters, except test weight, whereas regression analysis indicated 70.86, 85.21 and 73.45% contribution of tillers hill-1, dry matter production and leaf area index at harvest, respectively, to variations in grain yield. The organic finger millet growers should cultivate cv. ‘Kalua’ with application of FYM+NOC+VC @ 4.35+0.34+0.6 t ha-1 for achieving maximum yield and grain quality.

Genomic prediction for yield and malting traits in barley using metabolomic and near-infrared spectra

Key messageGenetic variation for malting quality as well as metabolomic and near-infrared features was identified. However, metabolomic and near-infrared features as additional omics-information did not improve accuracy of predicted breeding values.Significant attention has recently been given to the potential benefits of metabolomics and near-infrared spectroscopy technologies for enhancing genetic evaluation in breeding programs. In this article, we used a commercial barley breeding population phenotyped for grain yield, grain protein content, and five malting quality traits: extract yield, wort viscosity, wort color, filtering speed, and β-glucan, and aimed to: (i) investigate genetic variation and heritability of metabolomic intensities and near-infrared wavelengths originating from leaf tissue and malted grain, respectively; (ii) investigate variance components and heritabilities for genomic models including metabolomics (GOBLUP-MI) or near-infrared wavelengths (GOBLUP-NIR); and (iii) evaluate the developed models for prediction of breeding values for traits of interest. In total, 639 barley lines were genotyped using an iSelect9K-Illumina barley chip and recorded with 30,468 metabolomic intensities and 141 near-infrared wavelengths. First, we found that a significant proportion of metabolomic intensities and near-infrared wavelengths had medium to high additive genetic variances and heritabilities. Second, we observed that both GOBLUP-MI and GOBLUP-NIR, increased the proportion of estimated genetic variance for grain yield, protein, malt extract, and β-glucan compared to a genomic model (GBLUP). Finally, we assessed these models to predict accurate breeding values in fivefold and leave-one-breeding-cycle-out cross-validations, and we generally observed a similar accuracy between GBLUP and GOBLUP-MI, and a worse accuracy for GOBLUP-NIR. Despite this trend, GOBLUP-MI and GOBLUP-NIR enhanced predictive ability compared to GBLUP by 4.6 and 2.4% for grain protein in leave-one-breeding-cycle-out and grain yield in fivefold cross-validations, respectively, but differences were not significant (P-value > 0.01).

Genetic Variability for Grain Yield and its Component Traits in Little Millet [Panicum sumatrense L.]. Breeding Lines

Genetic Variability for Grain Yield and its Component Traits in Little Millet [Panicum sumatrense L.]. Breeding Lines

Valuation of Proceed PlusTMMD and Verification of CelestTop312.5FS Fungicides for the Control of Loose Smut of Wheat in Southeastern Ethiopia

Wheat is considered one of the utmost grown agricultural grain crops in the world to meet the food requirements of the domain’s population. In Ethiopia, wheat is one of key cereal crops whose area of cultivation and volume of produced boosted year after year. However, the production and productivity of wheat is truncated by various biotic and abiotic stresses. Among the biotic stresses, wheat loose smut disease caused by Ustilago tritici is one of quality and quantity incurring diseases but getting lower research attention. A field experiment was conducted with the objective to evaluate and verify the efficacy of fungicides against wheat loose smut diseases and put updated recommendation for users. It was executed at three loose smut hot spot locations, Meraro on-station, Kulumsa main center and Wonjigora on farm in 2023 main cropping season. Two different fungicides including the test fungicide (CelestTop312.5FS) and standard check (Proceed PlusTMMD), as well as untreated plot were used as a treatment. The trial was laid out in non-replicated plots at three sites where locations were considered as replica. The result of the research finding revealed that fungicide treated treatments significantly reduced loose smut diseases severity to the lowest level possible over the nil application. However, there was no statistically significant difference (p ≤ 5%) between the test and check fungicides in reducing loose smut diseases severity. Test fungicide revealed comparable level of efficacy on loose smut diseases severity reduction compared to the standard check. There was highly significant difference (p ≤ 5%) hectoliter weight between fungicide treatments and nil application of fungicide but insignificant variation in grain yield and thousand seed weight. Both fungicide treatments also revealed significant diseases management advantage than untreated plots. Thus, the newly verified fungicide CelestTop312.5 FS at a rate of 200ml fungicide with 1 to 2 liters of water as wetting agent for 100 Kg of wheat seed is found to be very effective in controlling loose smut diseases and is recommended for registration. Similarly, proceed PlusTM MD at a rate of 0.004 liter fungicide per kilogram of seed is suggested for further use for producers to control wheat loose smut diseases as sole or integrated disease management options.

A New Proposal for Soybean Plant Stand: Variation Based on the Law of the Minimum.

The hypothesis of this study is that it is possible to determine the plant stand in the soybean (Glycine max L. Merril) crop based on the spatial variability of management units, which are limiting factors in maximizing crop yield. Our objectives were as follows: (I) to evaluate the relationship between soil physical and chemical attributes to establish potential management units for variable-rate seeding; (II) to propose a method for varying plant stands based on the law of minimum soil nutrients; an (III) to relate the interaction between different plant stands on soybean grain yield, taking into account the interaction between the spatial variability of the mapped attributes. Field experiments were carried out on two plots over two agricultural years. The areas were seeded by randomly varying the soybean stand across strips in the first year. The most limiting soil nutrient was established and used, together with the soil CEC, to determine management units (MUs), which were also used to seed soybeans in VRT (Variable Rate Technology) in the same plots in the second year. MUs with the lowest restriction for maximizing yield were sown in the second year with the lowest plant stand. Data were processed using multivariate statistics. Our findings reveal that it is possible to establish MUs for seeding soybeans with different stands following the spatial variability of limiting soil nutrients according to the law of the minimum and thus increase the crop grain yield. Spatial variability of potassium (K) in the plot, identified as limiting, affected the spatial variability of grain yield. Decreasing plant stands in MUs with the lowest limitation level increases yield. However, increasing the stand in MUs with a higher limitation level can lead to increased intraspecific competition, affecting yield as well as increasing input costs.

Graphical Analysis of Multi-environmental Trials for Bread Wheat (Triticum aestivum L.) Grain Yield Based on GGE Bi-Plot Analysis

Bread wheat (Triticum aestivum L.) is a crucial crop in Ethiopia, and breeders test newly developed elite lines for superiority to existing cultivars to boost national productivity. The study was undertaken during the 2021–22 to 2022–23 cropping seasons at seven environments in optimum moisture areas of Ethiopian using 36 diverse and advanced bread wheat genotypes to evaluate the GEI by the graphical method of GGE biplot and to identify the genotypes with high mean yield performance and stability. Field experiments were conducted at the Adet, Asasa, Kulumsa, and Sinana research centers in Ethiopia. The experiments were planted in an alpha lattice design replicated three times in six rows of 2.5m long. Row-to-row distance and distance between blocks were 0.2m and 1.5m, respectively. The analysis of variance revealed that genotype, environment, and their interaction showed a highly significant effect on the yield as reflected in the GGE model and the GGE model indicated the suitability of the genotypes EBW202136 (33), Boru (1), and EBW202172 (12), with high mean yield and stability, whereas the genotypes EBW202185 (16) and Deka (36) produced high mean yield, but unstable. Likewise, the genotypes EBW202164 (27) and EBW202192 (29) produced low mean yield and unstable. The AMMI analysis of variance for grain yield across the environments showed that 17.26% of the total variation was attributed to genotypic effects, 64.03% to environmental effects, and 18.71% to GEI effects. Two mega environments were identified based on GGE biplot analysis and the which-won–model indicated the adaptation of genotypes Boru (1), EBW202159 (4), EBW202172 (12), EBW202171 (19), and EBW202136 (33) to first mega-environment and genotypes EBW202157 (3), EBW202166 (5), EBW202160 (6), EBW202162 (9), EBW202185 (16), Dursa (17) and Deka (36) in the second. These approaches allowed the identification of stable and high-yielding genotypes (EBW202136 (33) and EBW202172 (12)) which can be included in the national verification program, with a plan to release a new variety, and other genotypes with high yield could be utilized in breeding programs to further improve grain yield in bread wheat.

Performance of Black gram (Vigna mungo L. Hepper) under Different Levels of Phosphorus at Bardiya, Nepal

A field experiment was conducted during the spring season of 2023 in Badhaiyatal Rural Municipality-7, Bardiya district to evaluate the performance of black gram at various levels of phosphorus. A randomized complete block design (RCBD) consisting of four treatments and five replications were used for the study. The treatments consisted of different levels of phosphorous viz. 0 Kg ha-1 (control), 20 Kg ha-1, 40 Kg ha-1 and 60 Kg ha-1. Black gram var. BLG 0093-1 was sown at spacing of 40 cm x 5 cm in the plots sized 8 m2. All the growth parameters and yield obtained from each treatment were compared at 5% level of significance. The results showed significantly greater number of branches (13.84), flower buds (11.33), root length (16.27 cm) and secondary roots (16.05) at 60 Kg phosphorus per ha followed by 40 Kg ha-1. However, plant height, pods per plant, seeds per pod, weight of pods per plant, biomass and grain yield showed non-significant results at similar levels of phosphorus (60 Kg ha-1). Though non-significant, higher number of fruiting branch, number of nodules, effective nodules, 1000 grain weight and harvest index were observed at 40 Kg ha-1 phosphorus. The findings suggested a gradual increase in the yield with increasing levels of phosphorus with the highest gross return from 60 Kg ha-1 phosphorus. However, since the variation in grain yield among the treatments was not significant, the cost involved with the 60 Kg ha-1 was greater as compared to 40 Kg ha-1 phosphorus, greater benefit was observed with the latter. So, on grounds of greater net return and better BC ratio, 40 Kg ha-1 phosphorus seems to be more profitable for the farmers.

Inter and intra cross variability analysis in F3 generation of mung bean [Vigna radiata (L.) Wilczek

Abstract The present investigation was conducted to evaluate 12 crosses of mungbean genotypes for 11 characters to access the extent of genetic variability and to comprehend the heritable aspect of variation for grain yield and its related characters. All the 12 crosses were raised in a compact family block design with three replications in two environments during the kharif season of 2022. The analysis of variance indicated significant variations among the families for grain yield and all other characters in both environments. Conversely, differences among the progenies within a family varied depending on specific trait and environment (E1) to environment (E2). Inter cross variability revealed high GCV and PCV for primary branches per plant in both environments. High estimates of PCV were recorded for pods per plant and grain yield per plant in both environments and for clusters per plant in E2. High estimates of heritability were exhibited by primary branches per plant in both the environments and plant height in E1. High estimates of genetic advance as per cent of mean were observed for primary branches per plant and plant height in both environments. Inter cross variability revealed that the highest magnitude of heritability was recorded by family C1 and C7 for grain yield per plant in E1 and E2 respectively. The highest magnitude of genetic advance as per cent of mean was exhibited by family C3 for primary branches per plant in E1 and by family C12 for clusters per plant in E2. Keywords: Genetic variability, Inter cross variability, Heritability, Mungbean

Variation of grain zinc, phytate concentration and phytate : Zn molar ratio in unpolished and polished rice affected by foliar zinc application among Thai rice varieties

ABSTRACT This study evaluated the variation in grain yield and Zn concentration in rice after foliar application of Zn and the impact on its phytate : Zn molar ratio among 21 rice varieties. Rice varieties were grown under field conditions using a randomized complete block design with four replications over two cropping years in 2020 and 2021 during the rainy season. Foliar Zn application varied grain yield from −26.0 to 13.6% in 2020 and −14.7 to 30.4% in 2021 compared with non-foliar Zn. Grain Zn was varied from 0.5 to 31.1% in 2020 and from −5.9 to 47.8% in 2021 in unpolished rice and from 9.4 to 47.0% in 2020 and from 8.2 to 163.0% in 2021 in polished rice. The phytate : Zn molar ratio was decreased when foliar Zn was applied to the unpolished and polished rice, with a lower ratio in the polished rice. The phytate : Zn molar ratio decreased exponentially with increasing grain Zn concentration in the unpolished rice, while decreasing linearly with increasing grain Zn concentration in the polished rice. The results of this study suggest that foliar Zn application is a promising way to improve the grain Zn concentration and decrease the phytate : Zn molar ratio, thus being beneficial for human diets, but the effects of rice variety, cropping season, and the polishing process should be considered.

Analysis of genotype-by-environment interaction effect in barley genotypes using AMMI and GGE biplot methods

Genotype-by-environment interaction (GEI) analysis play a key role in any breeding program involving the development of new varieties for cultivation across various environments or in a specific region. The additive main effects and multiplicative interaction (AMMI) method and the GGE biplot are the two main statistical tools that have emerged to analyze GEI in multi-environment trials (METs). The main goal of the present study was to identify the best-performing and stable barley genotypes for the warm regions of Iran. For this purpose, 18 new advanced barley genotypes were investigated in five warm locations in Iran during two cropping seasons (2021–2023). In all experiments, test genotypes were evaluated in a randomized complete block design (RCBD) with three replications. Based on results, grain yield was significantly dependent on environments (E), genotypes (G), and GEI. The GEI effect was further divided into three principal component axes (IPCAs). The AMMI method identified genotypes G3, G9, G10, and G14 as ideal genotypes due to their low IPCA scores and high performances. In the GGE biplot analysis, the initial two PCAs accounted for 49.36 % of the total variation of grain yield, including both G and GEI effects. Based on averaged two-year data, genotypes G3, G4, G10, and G14 showed particular adaptability in the Zabol and Moghan regions. Moreover, the ranking of test environments showed good discriminatory and representative abilities for the Zabol and Moghan regions, so these environments constituted a mega-environment in Iran's warm climate. The genotype ranking indicated G3, G10 and G14 genotypes as the superior genotypes with the highest grain yield and stability in different test environments. Moreover, these results were confirmed by the results obtained by WAASB and WAASBY biplots. In conclusion, genotypes G3, G10 and G14 can be suggested for commercial usage and cultivation in various regions in Iran's warm climate.

Evaluation of Recently Released Finger Millet Varieties for Their Adaptability in West Haraghe Zone, Eastern Ethiopia

Finger millet is a major grain crop in the west hararghe zone. However, due to major constraints like lack of improved varieties and drought, the productivity is by far lower than the genetic potential of a crop in the study areas. Thus, current study initiated to obtain high-yielding and stable varieties. The study was conducted in districts of Habro, Mechara, and Gamachis of the west hararghe zone, using eight improved and one standard check finger millet varieties at 2020 main cropping seasons. The experiment was laid down in a randomized completely block design with three replications. Analysis of variance for grain yield across locations showed significant differences at p< 0.05. Further analysis of AMMI indicated that environments, varieties, and their interaction effects were significantly different. Even if, tested materials showed a significantly different grain yield across locations nevertheless, the GGE bi-plot analyses implied relatively high yielding and consistent across environments for varieties Bako-09, Gudetu, and Addis-01. Therefore, these varieties of finger millet were recommended for further evaluation at the farmer’s field.

Genotype-environment interaction for grain yield in maize (Zea mays L.) using the additive main effects and multiplicative interaction (AMMI) model

Genotype-environment interaction consists of the different response of individual genotypes resulting from changing environmental conditions. Its significance is a phenomenon that makes the breeding process very difficult. On the one hand, the breeder expects stable genotypes, i.e., yielding similarly regardless of environmental conditions. On the other hand, selecting the best genotypes for each region is one of the key challenges for breeders and farmers. The aim of this study was to evaluate genotype-by-environment interaction for grain yield in new maize hybrids developed by Plant Breeding Smolice Co. Ltd., utilizing the additive main effects and multiplicative interaction (AMMI) model. The investigation involved 69 maize (Zea mays L.) hybrids, tested across five locations in a randomized complete block design with three replications. Grain yield varied from 8.76 t ha–1 (SMH_16417 in Smolice) to 16.89 t ha–1 (SMH_16043 in Płaczkowo), with a mean yield of 13.16 t ha–1. AMMI analysis identified significant effects of genotype, environment, and their interaction on grain yield. Analysis of variance indicated that 25.12% of the total variation in grain yield was due to environment factor, 35.20% to genotypic differences, and 21.18% to genotype by environmental interactions. Hybrids SMH_1706 and SMH_1707 are recommended for further breeding programs due to their high stability and superior average grain yield.

Genotype by Environment Interaction and Stability Analysis for Grain Yield in White Seeded Tef [<i>Eragrostis tef</i> (zucc.)Trotter] Genotypes in Western Oromia, Ethiopia

Tef [<i>Eragrostis tef (Zucc.) Trotter </i>L.] is a most important cereal crop in Ethiopia in terms of production, consumption and cash. The study was carried out to investigate grain yield stability and genotype by environment interaction for 18 genotypes conducted in the potential high land areas of Western Oromia, Ethiopia for two consecutive years (2020 to 2021) using Randomized Complete Block Experimental Design with three replications. The study of variance for grain yield using the AMMI model indicated highly significant variation for genotypes, environment, and genotype-environment interactions. Environment accounted for 18.7% of the variance in grain yield, 17.9% for genotypes, and 61.5% for genotypes. The first IPCA component accounted for 47.9% of the interaction effect and revealed the two models were fit. Genotypes G15, G10, G4, G1, and G3 had the lowest AMMI stability value (ASV), indicating stability; genotypes G16, G14, G9, G7, G2, and G5 had the highest ASV value, indicating instability. From over all analysis genotype G1 and G3, showed a high mean grain yield, lowest GSI, ASV and stable compared to other genotypes in the study. As a result, G1 and G3 were identified as the best genotypes for future breeding programs and potential release in Western Oromia, Ethiopia's highlands.

Wheat yield and grain-filling characteristics due to cultivar replacement in the Haihe Plain in China.

The Haihe Plain plays an important role in wheat production and food security in China and has experienced continuous cultivar replacement since the 1950s.This study assessed the evolution of the yield and grain-filling characteristics of the main winter wheat cultivars in the Haihe Plain over the last seven decades (1950s to date). Cultivar characterization indicated that the increase in yield was negatively affected by spike number and positively affected by the number of kernels per spike before the 2000s and kernel weight after the 2000s. Field trials were conducted across two ecological zones over two consecutive wheatgrowing seasons. The results showed that genetic gains in grain yield, spike number, and kernel weight during 1955 to 2021 were 0.629%, 0.574%, and 0.332% year-1 on a relative basis or 39.12 kg ha-1, 24,350 hm-2, and 0.15 g year-1 on an absolute basis, respectively. However, the increase in the kernel number per spike was not significant. Moreover, cultivar replacement explained 25.6%, 12.8%, and 37.5% of the total variance in grain yield, spike number, and kernel weight, respectively. In summary, during the initial grain-filling stage, wheat cultivar replacement led to the shortening of grain-filling duration and rapid grain-filling rate. However, a longer active grain-filling duration was produced by prolonged durations of rapid and late grain-filling. Additionally, the experimental year had a greater effect on the kernel number, which explained 53.2% of the total variance. Ultimately, modern wheat cultivars had a greater kernel weight. Although the increase in kernel weight has affected grain yield during cultivar replacements in the Haihe Plain, the potential for further yield increase through kernel weight enhancement alone is limited. Consequently, future breeding efforts and cultivation practices should focus on improving spike traits and canopy architecture to enhance productivity.

Assessing drought tolerance in advance wheat genotypes using stress tolerance indices

Wheat, an important crop of Nepal, is significantly affected by drought, leading to severe yield losses. Thus, an experiment was conducted to assess effect of drought on wheat traits and to identify drought resilient genotypes comparing stress tolerance indices. Altogether seventy-two genotypes including checks were evaluated under drought and non-stress condition in an alpha-lattice design with two replications at the research block of National Rice Research Program, Hardinath, Nepal during winter of 2019/20. Analysis of variance revealed significant genotypic differences (p<0.01) in traits such as days to heading, anthesis, and maturity, plant height, flag leaf area, spike length, grains per spike, 1000-grain weight, and grain yield under both conditions. The combined analysis of variance showed that genotype, environment, and their interaction significantly influenced most traits. The environment was the dominant factor, accounting for 86.2% of the variation in grain yield, followed by genotype (9.5%) and genotype-environment interaction (4.3%). Among the nine quantitative traits assessed, grain yield was most severely affected due to drought, experiencing a substantial reduction of 63%. To assess drought tolerance, six indices (TOL, SSI, MP, GMP, HMP, and STI) were calculated based on grain yield data. Most indices identified genotypes NL1373, NL1308, NL1407, BL4868, and BL4947 as highly drought-tolerant. Among the indices, MP, STI, and GMP were the most reliable for measuring stress tolerance due to their strong positive correlation with yield under both conditions. These identified genotypes are promising candidates for breeding programs aimed at developing drought-resilient wheat varieties, thereby enhancing food security in drought-affected regions.

Comprehensive assessment of salt-alkali tolerance of different soybean varieties at the maturation stage

Soil salinization is a significant global challenge impacting food security and arable land safety. Biological amelioration for the development and utilization of salt-alkali land has gained attention in recent years but the breeding and selection of salt-alkali-tolerant crop varieties remain a challenge. Cultivated soybeans are moderately salt-tolerant crops; however, a precise threshold for salt-alkali conditions has not been determined and varies by genotype. Therefore, this study evaluated the salt-alkali tolerance of 15 different soybean varieties using correlation, principal component, membership function, and cluster analyses. The average soybean indices decreased with increasing salt-alkali stress concentrations. Under severe salt-alkali stress, the coefficients of variation for grain yield and biomass were significantly higher than those under mild salt-alkali stress. Plant height and node number were significantly positively correlated, whereas bottom pod height was significantly negatively correlated with branch number, effective pod number, and grain number per plant. Node and grain numbers per plant were significantly positively correlated. Branch number, effective pod number, grain number per plant, and hundred-grain weight were significantly and positively correlated. Effective pod number was significantly positively correlated with grain number per plant, biomass, and yield. Grain number per plant exhibited a significant negative correlation with hundred-grain weight. Biomass and yield were significantly positively correlated. The salt-alkali tolerance of 15 soybean varieties ranged from strong weak as follows: Cangdou 1438, 0734, 1426, 1418, 1453, 1327, 1846, 1412, 1817, 1301, 1821, 1819, 1815, 1814, and 1850. Salt-alkali-sensitive and salt-alkali-tolerant varieties were successfully identified.

CORRELATION AND REGRESSION ANALYSIS FOR YIELD & RELATED ATTRIBUTES IN F2 POPULATION OF BREAD WHEAT (Triticum aestivum L.) GENOTYPES

This research was carried out to study correlation & regression analysis in bread wheat at Southern Wheat and Barley Research Institute, Tandojam in RCBD with three replications. The results showed that most of the traits were significantly different at (P≤0.01) among the genotypes. Correlation analysis showed that spikelets per spike had a positive and highly significant association with biological yield per plan (r = 0.82**). Among the F2 crosses, days to 75% heading was significantly and positively associated with the grain yield (r = 0.80**). The trait tillers per plant had a positive association of significance with grain yield per plant (r = 0.86**) and biological yield per plant (g) (r = 0.84**). Regression analysis showed that the contribution rate of plant height to the change of grain yield was 0.47%; although the contribution to the variation of grain yield per plant was 08.21%. Spike length and spikelets per spike contributed 51.99% and 57.02% to the variation of grain yield per spike. The same character, grain yield was also contributed by grains per spike and seed index with the change of 58.99% and 78.36%. Above mentioned studies have shown that seed index, tillers per plant and spike length can be used to increase the grain yield of wheat genotypes to develop wheat varieties.

Genetic variability and path coefficient analysis among bread wheat (Triticum aestivum L.) genotypes for yield and yield‐related traits in bale highlands, southeastern Ethiopia

AbstractWheat (Triticum aestivum L.) is one of the most important strategic crops to overcome food security problems in the world including Ethiopia. This can be achieved through genetic improvement of the crop and its production practices. In this connection, information on the mutual association of traits is important for effective selection in wheat‐breeding programs. In 2021/2022 production year, a field experiment was undertaken to estimate the extent of genetic variability, path analysis, and association of yield and yield‐related characteristics for the selection of desirable wheat genotypes. A total of 64 bread wheat genotypes, including yellow rust‐resistant Boru (resistant), Hachalu (moderately resistant), and Madda‐Walabu (susceptible) as control, were used for the field experiment at Sinana and Agarfa Agricultural Research Centers. The experiment was laid out in a simple alpha lattice design. The mean analysis of variances showed highly significant differences (p < 0.001) for all traits among the tested genotypes. The value of the phenotypic coefficient of variance was higher than the genotypic coefficient of variance for all studied traits, indicating the presences of higher genetic variability among the tested genotypes. Grain yield had a significant positive correlation with days to maturity, grain‐filling period, plant height, kernels spike−1, thousand seed weight, and biological yield at both genotypic and phenotypic levels including spike length at phenotypic levels at both locations and with plant height, thousand seed weight, and biological yield at both locations at genotypic and phenotypic levels. Similarly, days to maturity and grain‐filling period also showed a significant positive phenotypic correlation with grain yield. The analyzed path coefficients at both genotypic and phenotypic levels also indicated how grain yield is influenced directly and indirectly by other yield and yield‐related traits. The result also showed the existence of good heritability values along with high genetic advance, higher phenotypic coefficient of variation and genotypic coefficient of variation for grain yield, biological yield, and grain‐filling period. This implies the true relationship among these traits and grain yield; therefore, due attention should be paid to such traits during selection for further improvement. Hence, the present study indicated the importance of the direct and indirect effects of the studied traits on yield and yield components which makes selection desirable for improving the grain yield of wheat and its genetic resistance against yellow rust in the study area.

Screening of Wheat Genotypes for Water Stress Tolerance Using Soil–Water Relationships and Multivariate Statistical Approaches

Drought stress constricts crop production around the world. Employing high-yielding cultivars with drought tolerance might be the ideal professional approach to coping with its detrimental outcomes. As a result, the current study was performed to investigate the sensitivity and tolerance of nine wheat genotypes to drought stress. In a randomized block design experiment, nine wheat genotypes were subjected to four water treatments: 100%, 85%, 70%, and 55% of the available water (AW). Four water regimes in two growing seasons were counted as eight environmental zones. The leaf’s water relations and photosynthetic pigment were estimated, as well as growth and yield parameters. Univariate and multivariate statistical approaches, including the new method of multi-trait genotype–ideotype distance (MGIDI), were used for evaluation. The analysis of variance revealed that genotype, environment, and their interactions had a highly significant effect on all traits. The same trend was shown by the additive main effects and multiplicative interaction (AMMI) analysis of variance for grain yield across the environments. The AMMI biplot study indicated that the G8 genotype is the most stable in terms of water stress. The G7 genotype can withstand droughts up to 55% of the available water, while the G8 and G3 genotypes can withstand droughts up to 70% of the available water. Based on all examined traits, this index was used to identify the stable genotypes G7, G8, and G3, which can therefore be suggested for cultivation during drought conditions. Furthermore, we found a positive correlation between the MGIDI, ANOVA, and tolerance index results, indicating that the same desirable genotypes of G7 and G8 were identified by these procedures as being highly tolerant and stable across a range of soil moisture conditions. Based on MGIDI analysis, we can recommend that the G7 genotype exhibits higher grain yield and yield-related traits with the best drought-tolerant indices.

Genotype by environment interaction and stability analysis of three agronomic traits in Kersting's groundnut (Macrotyloma geocarpum) using factor analytic modeling and environmental covariates

AbstractUnderstanding genotype by environment interaction (GEI) represents a challenge in Kersting's groundnut [Macrotyloma geocarpum (Harms) Maréchal and Baudet] breeding for selecting high‐performing and stable lines across environments. Here, we investigated GEI and stability in Kersting's groundnut using factor analytic (FA) based linear mixed models and environmental covariates. A total of 375 accessions were evaluated across 3 years (2017, 2018, and 2019) and two locations (Sékou and Savè) in Benin, generating five environments (E1, E2, E3, E4, and E5). The traits measured included days to 50% flowering (DFF), grain yield (YLD), and 100‐seed weight (HSW). The study generated multi‐environment values for grain yield and its components in Kersting's groundnut. The genetic correlations between pairs of environments ranged from −0.71 to 0.99. The genetic correlations between YLD and HSW indicated positive and moderate to high correlations in all environments. The FA analysis revealed that FA2 structure accounted for 93.9% of the genetic variability in DFF with factor 1 accounting for more than 90% of the environments variations. Two factors explained 87% of the genetic variance in grain yield, and 70% of the environments variability were clustered by factor 1. For HSW, two factors explained 85% of the genetic variance of the environments, and factor 1 accounted for 72.7%. Combining environmental covariates to FA models revealed that precipitation, temperature, and growth cycle duration were highly correlated to the environmental loadings of factor 1. Relative humidity and solar radiation showed moderate to high correlations with factor 2 loadings. Those covariates explained the high GEI among environments clustered by a given factor. Precipitations and temperatures affected the variations in grain yield. Finally, based on latent regression analysis, the accessions AF202, AF221, AF223, AF225, and AF256 were identified as accessions combining best performance for grain yield, early flowering, and 100‐seed weight, showing adaptability across environments and stability to some environments.