Environment Interaction For Grain Yield Research Articles

Study on G x E Interaction of Sorghum Lines (Sorghum bicolor L. Moench) for Grain Yield and Its Contributing Traits in Germplasm of Indore, India

The study was conducted to determine the stability of sorghum germplasm for grain yield as well as the amount and nature of genotype by environment interactions for grain yield in order to find and recommend stable high yielding variants. The trial was arranged in a randomized block design (RBD) in three replications. Germplasm were evaluated in 2 environments in Indore in 2019-2020 and 2020-2021 in main cropping seasons. 60 sorghum germplasm was evaluated under Thirteen morphological traits viz., Days to 50% flowering, Days to maturity, plant height (cm), Number of leaves per plant at the time of flowering, Leaf length of 4th leaf (cm), Leaf width of 4th leaf (cm), Panicle length (cm), Panicle width (cm), No. of Primary branches per panicle, No. of grains per primary branch, Grain yield/Plant (g), Leaf Area of 4th leaf (cm²), 100 seed weight (g) were recorded for 3 randomly selected plants for each germplasm per replication. Linear regression model for stability suggested by Eberhart and Russell was employed. Genotype x Environment interactions were found significantly in similar trend for the traits namely, days to 50% flowering, number of leaves per plant, leaf length of fourth leaf, leaf width of fourth leaf, panicle length, panicle width, number of primary branches per panicle, number of grains per primary branch, leaf area of the fourth leaf, grain yield per plant and 100 seed weight. It shows consistent results over years. Genotype x Environment interaction also revealed that the genotype SEB12025 found consistent for primary branches per panicle, grain yield. The germplasm E- 246 found suitable for seed weight, panicle width, Width of fourth leaf. Apart from POP-18, POP 27-1, EC-6, ERN-32, Gird-30, Gird 33-1, VKG 34/47, VKG 34/37, ER-15, EC- 16, ER-1, SEB 12025, E-207, ER-21, Gird-36, EGN-1, E-207, ER-3, Gird-41, E- 284,E-246, ER-3,Gird-41,Gird-5,ERN-32,GGUB-20, ELG-25,Sor 86,NCC-1,E-246 and ELG-31 no genotypes found consistent for any of the character. The characters which were found stable for a particular genotype should further be improved to develop stable and high-yielding genotypes for sustainable production. More locations and years could be taken to obtain a database about genotype x environment interaction to draw a more valid conclusion.

Delineation of Genotype X Environment Interaction for Grain Yield in Spring Barley under Untreated and Fungicide-Treated Environments

Barley (Hordeul vulgare L.) is the fourth most important cereal crop based on production and cultivated area. Biotic stresses, especially fungal diseases in barley, are devastating, incurring high possibilities of absolute yield loss. Identifying superior and stable yielding genotypes is crucial for accompanying the increasing barley demand. However, the identification and recommendation of superior genotypes is challenging due to the interaction between genotype and environment. Hence, the present investigation was aimed at evaluating the grain yield of different sets of spring barley genotypes when undergoing one of two treatments (no treatment and fungicide treatment) laid out in an alpha lattice design in six to seven locations for five years, through additive main effects and multiplicative interaction (AMMI), GGE biplot (genotype + genotype X environment), and stability analysis. The combined analysis of variance indicated that the environment was the main factor that contributed to the variation in grain yield, followed by genotype X environment interaction (GEI) effects and genotypic effects. Ten mega environments (MEs) with five MEs from each of the treatments harboured well-adapted, stable yielding genotypes. Exploiting the stable yielding genotypes with discreet use of the representative and discriminative environments identified in the present study could aid in breeding for the improvement of grain yield in spring barley genotypes.

AMMI biplot analysis for stability in early maturity group of rice (Oryza sativa L.)

Recent predictions on climate change indicate that episodes of unseasonal rains, cold stress, and high temperatures are expected to impact rice production and productivity. To obtain consistent yield across diverse environments, a rice variety should have adaptability and stability to fit into various growing seasons and locations. In the present investigation, AMMI model was employed to assess the stability of nine rice genotypes of the early maturity group across two summer and rainy seasons. Combined analysis of variance expressed a significant genotype, environment and genotype × environment interaction for grain yield and days to 50% flowering. The study also suggested environmental effect as the greatest part of the variation, followed by genotypic and genotype × environment interaction effects for these traits. Biplots and statistics of AMMI identified that G1, G3 and G5 were the most stable and adapted high yielding rice genotypes, while G9, G4 and G6 appeared to be the most stable genotypes with earliness. Hence, these genotypes could be used as directly as varieties or as donors in future breeding programmes for improving rice productivity in the early maturity group after evaluation under multi-location trials.

Genotype - Environment Interaction for Grain Yield In Wheat

Genotype - Environment Interaction for Grain Yield In Wheat

Genotype by environment interaction for grain yield in foxtail millet (Setarai italica) using AMMI model and GGE Biplot

Genotype by environment interaction for grain yield in foxtail millet (Setarai italica) using AMMI model and GGE Biplot

Assessing genetic diversity and aggregate genotype selection in a collection of cumin (Cuminum cyminum L.) accessions under drought stress: Application of BLUP and BLUE

Maintaining genetic diversity and aggregate traits selection in medicinal plants improves selection efficiency and provides the flexibility to adapt environmental stresses. The present studies aimed at evaluating genetic variation and assessing efficiency of three selection index models based on relative economic values of traits and a ranking based method in 30 cumin accessions under drought and well-watered conditions in 2015 and 2016 years. Drought stress regime applied at the flowering stage of growth cycle. The best linear unbiased estimation values (BLUE) were used for aggregate genotype selection in a desired direction in the Smith-Hazel I (SMH-I) and II (SMH-II), and Pesek-Baker (PSB) indices. The three selection indices did differ in assigning relative economic values for traits tested in cumin. Best linear unbiased predictions (BLUP) were used to predict rank summation index values (RSI). Grain yield (GY) ranged from 183.07 to 379.99 kg/ha and from 430.07 to 942.26 kg/ha in 2015 and 2016 experiments, respectively. Of the accessions tested in 2015, 20 with top rankings for GY were selected for further validation in 2016. Selections in 2015 resulted in 7.1% for plant height (PH) to 165.7% for plant weight (PW) progress in drought treatment in 2016. Among the traits, PH, thousand grain weight (TGW) and harvest index (HI) showed low changes between the two moisture regimes. Plant height with high heritability and significant correlation with GY showed low variation over seasons and watering regime treatments and is suggested for indirect selection of GY. Analysis of drought susceptibility index (DSI) showed that Kashmar with low DSI had low genotype × environment interaction for GY. Grains per plant (GPP) and biological yield traits had the highest response to selection under drought stress. The SMH-II showed higher selection efficiency in both seasons. Zarand had top rankings for RSI among accessions for aggregate traits tested under drought conditions in both years. Our results showed aggregate genotype selection is a refinement of selection which incorporates information of individual genotypes with genetic and association of traits. Overall, the efficiency of SMH II which maximized genetic gains in a desired direction was higher than SMH I and PSB indices that helps to achieve breeding goals in an uncertain number of generations in cumin.

Genotype-specific P-spline response surfaces assist interpretation of regional wheat adaptation to climate change

Abstract Yield is a function of environmental quality and the sensitivity with which genotypes react to that. Environmental quality is characterized by meteorological data, soil and agronomic management, whereas genotypic sensitivity is embodied by combinations of physiological traits that determine the crop capture and partitioning of environmental resources over time. This paper illustrates how environmental quality and genotype responses can be studied by a combination of crop simulation and statistical modelling. We characterized the genotype by environment interaction for grain yield of a wheat population segregating for flowering time by simulating it using the the Agricultural Production Systems sIMulator (APSIM) cropping systems model. For sites in the NE Australian wheat-belt, we used meteorological information as integrated by APSIM to classify years according to water, heat and frost stress. Results highlight that the frequency of years with more severe water and temperature stress has largely increased in recent years. Consequently, it is likely that future varieties will need to cope with more stressful conditions than in the past, making it important to select for flowering habits contributing to temperature and water-stress adaptation. Conditional on year types, we fitted yield response surfaces as functions of genotype, latitude and longitude to virtual multi-environment trials. Response surfaces were fitted by two-dimensional P-splines in a mixed-model framework to predict yield at high spatial resolution. Predicted yields demonstrated how relative genotype performance changed with location and year type and how genotype by environment interactions can be dissected. Predicted response surfaces for yield can be used for performance recommendations, quantification of yield stability and environmental characterization.

Genotype by environment interaction for main winter triticale varieties characteristics at two levels of technology using additive main effects and multiplicative interaction model

The aim of this study was to assess genotype by environment interaction for grain yield, plant height and thousand-grain weight in winter triticale cultivars by the additive main effects and multiplicative interaction (AMMI) model. The study comprised of ten winter triticale varieties i.e.: Algoso, Baltiko, Grenado, Magnat, Moderato, Pawo, Todan, Trimester, Trismart and Witon. Field trials were performed at seven locations in three years (21 environments) in a randomized complete block design, with two replicates at two levels of cultivation technology (standard – A1 and intensive – A2). Field experiments were carried out as part of Post Registration Variety Trials in Wielkopolska region. AMMI analyses revealed significant genotype and environmental effects as well as genotype by environmental interaction with respect to all three observed traits in both levels of cultivation intensity. The cultivars Algoso, Baltiko and Trimester are recommended for further inclusion in the breeding programs because of their stability and good average values of observed traits.

GWAS revealed effect of genotype \xd7 environment interactions for grain yield of Nebraska winter wheat

BackgroundImproving grain yield in cereals especially in wheat is a main objective for plant breeders. One of the main constrains for improving this trait is the G × E interaction (GEI) which affects the performance of wheat genotypes in different environments. Selecting high yielding genotypes that can be used for a target set of environments is needed. Phenotypic selection can be misleading due to the environmental conditions. Incorporating information from phenotypic and genomic analyses can be useful in selecting the higher yielding genotypes for a group of environments.ResultsA set of 270 F3:6 wheat genotypes in the Nebraska winter wheat breeding program was tested for grain yield in nine environments. High genetic variation for grain yield was found among the genotypes. G × E interaction was also highly significant. The highest yielding genotype differed in each environment. The correlation for grain yield among the nine environments was low (0 to 0.43). Genome-wide association study revealed 70 marker traits association (MTAs) associated with increased grain yield. The analysis of linkage disequilibrium revealed 16 genomic regions with a highly significant linkage disequilibrium (LD). The candidate parents’ genotypes for improving grain yield in a group of environments were selected based on three criteria; number of alleles associated with increased grain yield in each selected genotype, genetic distance among the selected genotypes, and number of different alleles between each two selected parents.ConclusionAlthough G × E interaction was present, the advances in DNA technology provided very useful tools and analyzes. Such features helped to genetically select the highest yielding genotypes that can be used to cross grain production in a group of environments.

Interpretation of genotype × environment interaction for grain yield of barley using the GGE biplot method

The identification of the most favorable cultivar(s) with high yield and stable performance is usually done based on the analysis of the genotype × environment (GE) interaction. The yield stability of 16 barley lines with two check varieties was studied in a randomized complete block design with four replications across three years at five locations in a multi-environment trial layout. The dataset was analyzed with a GGE (genotype main effect (G) + GE interaction) biplot method. Results indicated that the first two principal components (PCs) explained 81, 78 and 71% of the GGE sum of squares for 2017, 2018 and 2019 growing seasons, respectively. According to the average environment coordinate abscissa, G2, G13 and G18 were the best genotypes in terms of grain yield in years 2017 and 2018 while genotypes G2, G7 and G14 were the highest yielding genotypes in 2019. When both yield performance and stability were considered simultaneously, the G2 and G13 genotypes in 2017 and G2, G8 and G13 in 2018, were closer to the ideal genotype. In 2019, G2, G7 and G14 were the best in terms of grain yield and stability. In the which-won-where the five locations in 2017 fell into four sectors with different winning genotypes as G2, G5, G14 and G13. In 2018, the five locations fell into three sectors in which G2, G4 and G17 were the highest yielding genotypes while in 2019, locations were positioned in four sectors and G2, G7, G10 and G13 were chosen as the winning genotypes. However, for practical use of the “which-won-where” pattern, the mean performance of genotypes over three years in the five test locations was taken into account. Although the results revealed six mega-environments, by neglecting small differences, we can assume only one mega-environment in which G2 (the check variety Khorram) was the best performing genotype.

Evaluation of genotype × environment interaction for grain yield of promising genotypes of rice (Oryza sativa L.) derived from mutation induction using the GGE-biplot method

The existence of genotype × environment interaction complicates the evaluation of cultivar performance and reduces gain to selection. One of the multivariate methods for interpreting genotype by environment interaction is GGE-Biplot, in which the main effect of genotype and genotype by environment interaction are investigated simultaneously. In this study, 13 mutant genotypes of rice along with three check cultivars Tarrom-Mahalli, Tarrom-Jelodar and Neda were evaluated for grain yield stability in the two locations of Sari and Tonekabon during the years 2016 and 2017 using randomized complete block design with three replications within each environment. The results of GGE-biplot analysis showed that the two first components explained 92.52% of the total yield variation. According to the polygon view, all four environments of the experiment were located in the place that the Neda cultivar was at the top. Genotypes 33, 30, 26, 31 were highly stable genotypes and genotypes 18, 16 and 25 were highly unstable. In this study, we found only one mega-environment. Also following Neda and Jelodar cultivars, genotype 31 was closest to the ideal genotype. Ton 95 was the most desirable environment.

Evaluation of Genotype -Environment Interaction for Grain Yield of Chickpea Genotypes (Cicer arietinum L.) in Cold Agro-Climate Zone of Iran by GGE Biplot Method

Evaluation of Genotype -Environment Interaction for Grain Yield of Chickpea Genotypes (Cicer arietinum L.) in Cold Agro-Climate Zone of Iran by GGE Biplot Method

Genetic variability, Genotype × Environment interaction for grain yield of wheat (Triticum aestivum) backcross inbred lines population under different moisture regimes

Drought stress is well known phenomenon that affects the productivity of wheat (Triticum aestivum L). Knowledge on genetic variation, genotype × environment interaction and association between physiological and yield component traits is crucial for the development of improved varieties having high yield and water use efficiency. The present study consists of 280 backcross inbred lines (BILs) population evaluated for grain yield and morpho-physiological traits for two years at three locations. Combined ANOVA unfolded significant variability among traits in BILs population for yield and morpho-physiological traits.Grain yield showed significant association with normalized difference vegetation index (NDVI), soil plant analysis development (SPAD), thousand grain weight (TGW), and canopy temperature (CT). The genotype, environment and genotype ×environment interaction for yield was highly significant (p< 0.01). ASV (AMMI stability value) was calculated and top 29 genotypes were selected and further analyzed with AMMI and GGE biplot analysis for dissecting out genotype × environment interaction. The results classified genotypes G82, G202, G234, G263, G6, G192 and G77 are most stable and high yielding genotypes.

Grain yield, anthesis-silking interval and drought tolerance indices of tropical maize hybrids

Abstract Water deficit stress is the abiotic factor with the highest impact on crop yield. The objective of this study was to evaluate grain yield (GY), the anthesis-silking interval (ASI) and drought tolerance indices in maize hybrids. We evaluated GY and the ASI of 86 hybrids under two moisture levels (normal irrigation and water stress) for three consecutive years. The stress susceptibility index, water stress tolerance, drought resistance coefficient, drought resistance index, stress tolerance index and harmonic mean were evaluated. There were significant hybrid x environment interactions for GY and the ASI. Differences in the ASI among environments ranged from 0 to 5 days. The hybrids P3862, 1I873, 1I923, 1I862 and 1J1211 showed high GY, associated with the highest drought tolerance indices. The stress tolerance index and harmonic mean indices can be used to identify higher-yielding maize hybrids in environments with and without water restriction.

Genotypes × environment interaction for grain yield of finger millet under hilly region of Nepal

Genotypes × environment interaction for grain yield of finger millet under hilly region of Nepal

Genotype-by-environment interactions for grain yield of Valencia groundnut genotypes in East and Southern Africa

Grain yield is a quantitatively inherited trait in groundnut (Arachis hypogea L.) and subject to genotype by environment interactions. Groundnut varieties show wide variation in grain yield across different agro-ecologies. The objectives of this study were to evaluate Valencia groundnut genotypes for yield stability and classify environments to devise appropriate breeding strategies. Seventeen multi-location trials were conducted in six countries, viz., Malawi, Tanzania, Uganda, Zimbabwe, Mozambique and Zambia, from 2013 to 2016. The experiments were laid out following a resolvable incomplete block design, with two replications at each location (hereafter referred to as ‘environments’) using 14 test lines and two standard checks. The additive main effects and multiplicative interaction (AMMI) analysis was conducted. Variation attributable to environments, genotypes and genotype × environment interaction for grain yield was highly significant (P<0.001). Genotype, environment and genotype × environment interactions accounted for 7%, 53 % and 40% of the total sum of squares respectively. Superior-performing genotypes possessing high to moderate adaptability and stability levels included ICGV-SM 0154, ICGV-SM 07539, ICGV-SM 07536, ICGV-SM 7501, ICGV-SM 99568 and ICGV SM 07520. Nachingwea 2013 in Tanzania, Nakabango 2014 in Uganda and Chitedze 2015 in Malawi were the most representative and discriminative environments. Considering the implications of interactions for Valencia groundnut breeding in East and Southern Africa we propose that different varieties should be targeted for production in different environments and at the same time used for breeding in specific environments.

Additive Main Effect and Multiplicative Interactions (AMMI) and Regression Analysis in Sorghum [<i>Sorghum bicolor</i> (L). Moench] Varieties

Background and justification: Ethiopia is the third largest sorghum producer in Africa next to Nigeria and Sudan. Shortage of widely adapted and stable high yielding variety is one of the major bottlenecks for production and productivity of sorghum in the country. Grain yield performance is not the only parameter for selection as a genotype with the highest grain yield would not necessarily mean stable and adaptable across location and years. Eberhart and Russell (1996) and AMMI model could be the preferable tools to identify stable, high yielding and adaptable genotype (s) for wider or specific environments. Objectives: To identify stable high yielding sorghum varieties that could be adapted for wider and/or specific environments and make recommendations for further demonstration and production in the test environments and similar agro ecologies.Material and methods: A total of 21 released sorghum varieties and a local check were evaluated at three locations in western Ethiopia (Bako, Gute, Biloboshe) and two locations in eastern Ethiopia (Mechara and Mieso) in 2017 main cropping seasons. The trial was arranged in a randomized complete block design (RCBD) in three replications. Summary of results and application of the study: The combined analysis of variance revealed highly significant effect of environment and genotype by environment interactions for grain yield. This indicated that the tested varieties showed inconsistent grain yield performance across locations. Birmash variety gave the highest grain yield with average yield of 3.5 ton ha-1 but specifically adapted to Gute, Biloboshe and Mechara. Baji was the second high yielding variety with mean grain yield of 3.3 ton ha-1 and relatively with wider adaptability. The first two IPCAs accounted for a total of 88.64% of the interaction sum square. In general, deviation from regression coefficient, AMMI stability value and genotype selection index revealed that Baji, Birmash, Emahoy, IS9302 and Gambella-1107 were relatively stable varieties with optimum grain yield and therefore recommended for further demonstration and popularization in the test locations and areas with similar agro-ecologies.Keywords: AMMI, ASV, IPCAs, Sorghum

Genotype by Environment Interaction for Grain Yield of Barley Mutant Lines

Abstract The aim of this study was to examine the genotype by environment interaction for grain yield and to identify high-yielding and stable mutant lines of 6-rowed winter barley under different growing seasons. The study was carried out during 7 growing seasons from 2010 – 2011 to 2016 – 2017 in the experimental field of the Institute of Agriculture – Karnobat, Southeastern Bulgaria. Fourteen advanced mutant lines and the check variety Vesletc were studied using a complete block design with 4 replications. The AMMI analysis of variance indicated that 20.54% of the variation for grain yield was explained by the effect of genotype and 37.34% and 42.12% were attributable to the environmental effects and genotype by environment interaction. The magnitude of the genotype by environment interaction was two times larger than that of genotypes, indicating that there was a substantial difference in genotype response across environments. The AMMI and GGE biplot analyses identified G9 as the highest yielding and stable genotype. This mutant line can be recommended for further evaluation for variety release. The mutant lines G6, G13 and G15 were suggested for inclusion in the breeding program of winter barley due to its high grain yield and intermediate stability.

Genotype by Environment Interaction for Grain Yield in Spring Barley Using Additive Main Effects and Multiplicative Interaction Model

Monoculture and use of disease resistant varieties on large scale usually leads to selection of new pathogen races able to overcome the resistance. The use of variety mixtures can significantly improve the control of the disease and provides stable yield among different environments. The objective of this study was to assess genotype by environment interaction for grain yield in spring barley genotypes grown in two places different in terms of soil and meteorological conditions by the additive main effects and multiplicative interaction model. The study comprised 25 spring barley genotypes (five cultivars: Basza, Blask, Skarb, Rubinek and Antek, and 20, two- and three-component mixtures), analyzed in eight environments (compilations of two locations and four years) through field trials arranged in a randomized complete block design, with three replicates. Grain yield of the tested genotypes varied from 32.88 to 74.31 dt/ha throughout the eight environments, with an average of 54.69 dt/ha. In the variance analysis, 68.80% of the total grain yield variation was explained by environment, 6.20% by differences between genotypes, and 7.76% by genotype by environment interaction. Grain yield is highly influenced by environmental factors.

Identification of stable genotypes and genotype by environment interaction for grain yield in sorghum (Sorghum bicolor L. Moench)

AbstractSorghum is a staple food crop in Niger and its production is constrained by sorghum midge and the use of low yielding, local sorghum varieties. To improve sorghum productivity, it is crucial to provide farmers with high yielding sorghum cultivars that are resistant to midge. We evaluated 282 genotypes in four environments of Niger Republic. Alpha (0.1) lattice with two replications was the experimental design. Genotype and genotype by environment (GGE) biplot analysis was used to study grain yield (GY) stability and G × E interactions. The results revealed that two distinct mega environments were present. Genotype L232 was the best genotype for GY in the first planting date at Konni and the first and second planting dates (PDs) at Maradi. Genotype L17 was the best for GY in the second PD at Konni. The second PD at Konni was the most discriminating environment while the first PD at Konni is suitable for selecting widely adapted genotypes for GY.