Genotype X Environment Interaction Research Articles

Assessment of Genotype X Environment Interaction and Seed Yield Stability in Sesame (Sesamum indicum L.) Using AMMI Analysis

Sesame is considered as an important oilseed crop in terms of nutritional and medicinal properties. Seed yield of any crop is highly labile to environmental factors, which may cause significant reduction in the yield performance. To get substantial stable yield production in crops like sesame, there is urgent need to identify stable genotypes that perform well under different environmental conditions. Thus, the present study has been planned with the aim to assess genotype x environment interaction (GEI) and stability analysis for seed yield per plant using additive main effect and multiplicative interaction (AMMI) model. The experiment involved evaluation of 20 sesame genotypes in randomized complete block design (RCBD) with three replications under three artificially created environments by providing different levels of fertilizers. The environment wise analysis of variance (ANOVA) revealed significant genotypic variance across all the environments. Additionally, AMMI ANOVA revealed significant environment, genotypes and GEI components. GEI component was further classified to interaction principal components (IPC), IPC1 and IPC2, which explain 66.18% and 33.82% of variance due to GEI, respectively. Further, through different stability indices (ASV and GSI) and AMMI biplots, it was revealed that genotype RT-346 and RT-351 are considered as stable genotypes and these can be further recommended to farmer under varying fertility conditions.

Genotype by environment (G × E) interaction analysis for seed yield and other contributing traits in linseed (Linum usitatissimum L.) across conventional and zero budget natural farming production systems in north-western Himalayas

Development of high-yielding and stable linseed (Linum usitatissimum L.) genotypes in Himachal Pradesh under zero budget naturalfarming system (ZBNF) is an absolute need. A study was conducted to evaluate 30 elite genotypes of linseed for yield and stability across sixteen different environments over two years using Additive main effects and a multiplicative interaction model. For seed yield the environment, genotype and genotype x environment interaction (GEI) effects were highly significant (p <0.001), with contributions to total observed variation of 89.74, 0.81 and 8.08%, respectively. Out of the two production systems, ZBNF was observed to show consistently poor mean yields as compared to the conventional system across all locations. However, locations Kangra and Dhaulakaun performed better under the ZBNF system than Palampur and Bajaura. ZBNF was also identified as less discriminating with weak interactive forces. As per Eberhart and Russell model, the most stable and high-yielding genotype was Surbhi whereas, as per AMMI model, the ASV and GSI values indicated Giza-7 (G18), KL-285 (G16), KL-311 (G1) and Surbhi (G28) as stable and high in grain yield. For yield attributing traits such as primary branches per plant genotypes Janki (G27) and KL-314 (G4), for secondary branches per plant genotypes KL-263 (G10) and Janki (G27), for seeds per capsule genotypes KL-279 (G13), Binwa (G30) and Him Alsi-2 (G19), for trait biological yield per plant Jeewan (G22), for harvest index KL-236 (G6) and Him Alsi-1 (G26) and for 1000 seed weight genotypes Him Alsi-1 (G26) and KL-285 (G16) showed, high stability along with high mean performance and therefore could be selected. Genotype KL-284 (G15) showed specific adaptation under ZBNF system of Bajaura and is therefore recommended for production under respective environments after further evaluation

Evaluation of elite soybean genotypes for yield and rust resistance in Southern Zone of India

The genotype x environment interaction (GEI) is an important aspect of plant breeding program as it decides the general and specific adaptation of a variety to the varied environments. Higher magnitude of GEI were noted in soybean with significant influence on yield. In the present study fourteen genotypes were evaluated at six locations in Southern Zone of India (Adilabad, Bangalore, Bidar, Dharwad, Kasbe Digraj and Pune) during Kharif 2017, 2018 and 2019. Across locations, DSb 34 recorded 21 % and 18 % increased yield over zonal check (DSb 21) and rust susceptible check (JS 335), respectively. DSb 34 also exhibited highly resistant reaction to rust disease at hotspot location and can be used as donor in soybean disease resistance improvement.

A simulation-based assessment of the efficiency of QTL mapping under environment and genotype x environment interaction effects.

The objective of this simulation-based study was to assess how genes, environments, and genotype x environment (GxE) interaction affect the quantitative trait loci (QTL) mapping efficiency. The simulation software performed 50 samplings of 300 recombinant inbred lines (RILs) from a F2, which were assessed in six environments. The RILs were genotyped for 977 single nucleotide polymorphisms (SNP) and phenotyped for grain yield. The average SNP density was 2 cM. We defined six QTLs and 190 minor genes. The trait heritability ranged from 30 to 80%. We fitted the single QTL model and the multiple QTL model on multiple phenotypes. The environment and complex GxE interaction effects led to a low correlation between the QTL heritability and power. The single- and across-environment analyses allowed all QTLs be declared, with an average power of 28 to 100%. In the across-environment analysis, five QTLs showed average power in the range 46 to 82%. Both models provided a good control of the false positive rate (6%, on average) and a precise localization of the QTLs (bias of 2 cM, on average). The QTL power in each environment has a high positive correlation with the range between QTL genotypes for the sum of the additive, environment, and GxE interaction effects (0.76 to 0.96). The uncertainty about the magnitude and sign of the environment and GxE interaction effects makes QTL mapping in multi-environment trials unpredictable. Unfortunately, this uncertainty has no solution because the geneticist has no control over the magnitude and sign of the environment and GxE interaction effects. However, the single- and across-environment analyses are efficient even under a low correlation between QTL heritability and power.

Identification of QTNs, QTN-by-environment interactions for plant height and ear height in maize multi-environment GWAS

Plant height (PH) and ear height (EH) are important traits associated with biomass, lodging resistance, and grain yield in maize. There were strong effects of genotype x environment interaction (GEI) on plant height and ear height of maize. In this study, 203 maize inbred lines were grown at five locations across China’s Spring and Summer corn belts, and plant height (PH) and ear height (EH) phenotype data were collected and grouped using GGE biplot. Five locations fell into two distinct groups (or mega environments) that coincide with two corn ecological zones called Summer Corn Belt and Spring Corn Belt. In total, 73,174 SNPs collected using GBS sequencing platform were used as genotype data and a recently released multi-environment GWAS software package IIIVmrMLM was employed to identify QTNs and QTN x environment (corn belt) interaction (QEIs); 12 and 11 statistically significant QEIs for PH and EH were detected respectively and their phenotypic effects were further partitioned into Add*E and Dom*E components. There were 28 and 25 corn-belt-specific QTNs for PH and EH identified, respectively. The result shows that there are a large number of genetic loci underlying the PH and EH GEIs and IIIVmrMLM is a powerful tool in discovering QTNs that have significant QTN-by-Environment interaction. PH and EH candidate genes were annotated based on transcriptomic analysis and haplotype analysis. EH related-QEI S10_135 (Zm00001d025947, saur76, small auxin up RNA76) and PH related-QEI S4_4 (Zm00001d049692, mads32, encoding MADS-transcription factor 32), and corn-belt specific QTNs including S10_4 (Zm00001d023333, sdg127, set domain gene127) and S7_1 (Zm00001d018614, GLR3.4, and glutamate receptor 3.4 or Zm00001d018616, DDRGK domain-containing protein) were reported, and the relationship among GEIs, QEIs and phenotypic plasticity and their biological and breeding implications were discussed.

Phenotypic evolution in durum wheat (Triticum durum Desf.) based on SNPs, morphological traits, UPOV descriptors and kernel-related traits.

Durum wheat is a worldwide staple crop cultivated mainly in the Mediterranean basin. Progress in durum wheat breeding requires the exploitation of genetic variation among the gene pool enclosed in landraces, old cultivars and modern cultivars. The aim of this study was to provide a more comprehensive view of the genetic architecture evolution among 123 durum wheat accessions (41 landraces, 41 old cultivars and 41 modern cultivars), grown in replicated randomized complete block in two areas, Metaponto (Basilicata) and Foggia (Apulia), using the Illumina iSelect 15K wheat SNP array and 33 plant and kernel traits including the International Union for the Protection of new Varieties of Plants (UPOV) descriptors. Through DAPC and Bayesian population structure five groups were identified according to type of material data and reflecting the genetic basis and breeding strategies involved in their development. Phenotypic and genotypic coefficient of variation were low for kernel width (6.43%) and for grain protein content (1.03%). Highly significant differences between environments, genotypes and GEI (Genotype x Environment Interaction) were detected by mixed ANOVAs for agro-morphological-quality traits. Number of kernels per spike (h2 = 0.02) and grain protein content (h2 = 0.03) were not a heritability character and highly influenced by the environment. Nested ANOVAs revealed highly significant differences between DAPC clusters within environments for all traits except kernel roundness. Ten UPOV traits showed significant diversity for their frequencies in the two environments. By PCAmix multivariate analysis, plant height, heading time, spike length, weight of kernels per spike, thousand kernel weight, and the seed related traits had heavy weight on the differentiation of the groups, while UPOV traits discriminated moderately or to a little extent. The data collected in this study provide useful resources to facilitate management and use of wheat genetic diversity that has been lost due to selection in the last decades.

Performance of sweet chili pepper (Capsicum chinense Jacq.) landraces in three agricultural zones of Puerto Rico

Capsicum chinense is a small aromatic pepper native to the Amazon and widely used in the Caribbean Basin. Most types of C. chinense are highly pungent, but landraces in Puerto Rico generally have little or no pungency. This “sweet chili pepper” (or ají dulce, as it is known on the Island) is an integral part of the local cuisine. Ten sweet chili pepper lines derived from non-pungent landraces were evaluated in Lajas, Juana Díaz and Corozal, Puerto Rico, in 2009. The lines represented a diversity of fruit types common on the Island. Plants in Juana Díaz grew to about twice the height of those in Lajas and Corozal. Fruit yield varied from 809 g/plant in Lajas to 1,420 g/plant in Juana Díaz. Planting density (number of plants per hectare) was greater at Corozal compared to the other two locations, resulting in a yield per hectare similar to that of Juana Díaz (10,210 kg/ha in Juana Díaz; 10,112 kg/ha in Corozal). Yield was much lower in Lajas (6,400 kg/ha). Average fruit weight was greatest at Juana Díaz (12.1 g) and lowest at Corozal (10.0 g). Plant height at 11 weeks was weakly correlated (r = 0.33) with fruit yield. Plants infected by at least one of three genera of viruses, Cucumovirus, Tobamovirus and Potyvirus, were found at each location. Seed weight per 500 seeds averaged 2.34 g. The average fruit produced about 23 seeds. Genotype x environment (GxE) interaction (= line x location) was highly significant for height, number of fruit and fruit yield, but not for average fruit weight. Because of GxE interaction, the best performing lines varied, depending on location. The presence of GxE interaction presents challenges to a sweet chili pepper breeding program. Although it may be difficult to develop cultivars that perform equally well over diverse environments, improved cultivars with virus resistance should result in increased production. More research is needed to better understand the impact that variation in planting density has on sweet chili pepper yields.

Application of multivariate analysis for assessment of stability in winter safflower (Carthamus tinctorius L.) genotypes under rainfed conditions

Assessment of genotype x environment interaction (GEI) is an important constituent of cultivar selection process in multi-environment traits (MET). A set of 15 genotypes of winter safflower (Carthamus tinctorius L.) were evaluated for grain yield, compatibility, yield stability and GEI in diverse environments under rainfed conditions. The analysis of variance conducted through AMMI method showed significant variation in grain yield with respect to mean squares of environments, genotypes, and GEI. The two main components, IPCA1 and IPCA2, accounted for 85.29% of the variation and GEI, indicating almost high biplot validity. The polygon biplot analysis identified six superior and stable genotypes and two mega-environments

Genotype x environment interaction and climate sensitivity in growth and wood density of European larch

European larch is an ecologically and economically important tree species planted across a wide range of environments. Understanding its response to the rapidly changing climate is essential to developing efficient gene resource management strategies in line with the assisted migration methodology. In this study, we quantified the genotype x environment interaction (GxE) of a larch provenance in a multi-environmental trial analysis using factor analytic (FA) modeling. We analyzed the mean annual height increment (MAI-H), the mean annual diameter increment (MAI-DBH), and wood density (PP). Additionally, we evaluated the effect of several environmental variables on these traits. The genetic correlations between sites varied from −0.53 to 0.99, −0.87 to 0.99, and −0.17 to 0.99 for MAI-H, MAI-DBH, and PP, respectively. This indicates the presence of low to high GxE. The growth traits showed more GxE signal than wood density. Narrow-sense heritability estimates were 0.41, 0.16, and 0.35 for MAI-H, MAI-DBH, and PP, respectively. The FA modeling of order 2 explained 77.4%, 67.9%, and 77.3% of the genetic variance for MAI-H, MAI-DBH, and PP, respectively. For MAI-H, the latent regression plots for the predicted site means indicate a significant positive correlation with the first factor loading. At the individual genotype level, the latent regression plots indicated high levels of GxE, especially for growth traits. Significant and relatively high correlations were found between several EVs and the factor loadings of the FA models, suggesting their possible influence on the genotypic performance across environments. Regarding MAI-H, elevation, averaged monthly mean temperature, precipitation seasonality, and stand age were correlated with the first factor loading. Annual temperature range with both the first and second factor loadings. For MAI-DBH, the mean diurnal range was correlated with the first factor loading. In the case of PP, we found that elevation was correlated with the second factor loading. Overall, this approach successfully modeled the GxE and identified environmental variables likely affecting it. Therefore, we conclude that this methodology is a valuable tool for selecting suitable genotypes adapted to specific environmental conditions.

Strategies for identifying stable lentil cultivars (Lens culinaris Medik) for combating hidden hunger, malnourishment, and climate variability.

Iron and zinc malnutrition is a global humanitarian concern that mostly affects newborns, children, and women in low- and middle-income countries where plant-based diets are regularly consumed. This kind of malnutrition has the potential to result in a number of immediate and long-term implications, including stunted growth, an elevated risk of infectious diseases, and poor development, all of which may ultimately cause children to not develop to the fullest extent possible. A determination of the contributions from genotype, environment, and genotype by environment interactions is necessary for the production of nutrient-dense lentil varieties that offer greater availability of iron and zinc with a high level of trait stability. Understanding the genotype and environmental parameters that affect G x E (Genotype x Environment) interactions is essential for plant breeding. We used GGE(Genotype, Genotype x Environment interactions) and AMMI (Additive Main effects and Multiplicative Interaction) models to study genetic stability and GE(Genotype x Environment interactions) for grain Fe, Zn, Al, and anti-nutritional factors like phytic acid content in sixteen commercially produced lentil cultivars over several different six geographical locations across India. Significant genetic variability was evident in the Fe and Zn levels of different genotypes of lentils. The amounts of grain iron, zinc, and phytic acid varied from 114.10 to 49.90 mg/kg, 74.62 to 21.90 mg/kg, and 0.76 to 2.84 g/100g (dw) respectively. The environment and GE (Genotype x Environment interactions) had an impact on the concentration of grain Fe, Zn, and phytic acid (PA). Heritability estimations ranged from low to high (53.18% to 99.48%). The study indicated strong correlation between the contents of Fe and Zn, a strategy for simultaneously increasing Fe and Zn in lentils may be recommended. In addition, our research revealed that the stable and ideal lentil varieties L4076 (Pusa Shivalik) for Fe concentration and L4717 (Pusa Ageti) for Zn content, which have lower phytic acid contents, will not only play an essential role as stable donors in the lentil bio-fortification but will also enable the expansion of the growing area of bio-fortified crops for the security of health and nutrition.

Genotype x Environment Interactions for Oil Content in Cotton (Gossypium hirsutum L.) Cultivars Grown in Northern Cameroon

Cotton breeding programs are mainly focus on improving fiber although the oil extracted from cotton seeds is the fifth vegetable oil consumed in the world due to its good quality. The purpose of this study was to evaluate six cultivars of Gossypium hirsutum regarding their oil content on four locations in the northern Cameroon during two consecutive seasons, in order to select stable genotypes for high oil content. In each location, the experimental design was a completely randomized block with three replications. The determination of the oil content of the cottonseeds was done by the Soxhlet method. Genotype x environment interaction (GEI) and analysis of stability of the varieties were determined by different methods using GEST 98 package. The variability among genotypes was high across environments for oil content (20.34% in Berem to 26.08% in Kourgui). The top ranked lines for oil were Irma Q302 (26.61%) and Irma A2249 (26.40%). This showed that there is genetic and environmental variability that can be exploited for the selection of genotypes at each site. The broad-sense heritability for oil content varied from 0.79 (Pitoa) to 0.83 (Berem) and expected genetic gain ranged from 14% to 23% with an overall average of 19%. Genotypes, environment and GEI effects were all significant and accounted respectively 35.65%, 43.41% and 20.93% of the total variation. Stability analysis identified high-yielding genotype Irma Q302 as specifically adapted to favourable environments of Kourgui and Pitoa.

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.

Caracterização agronômica e interação genótipo ambiente de quinoa nas condições do Brasil e da Colômbia

ABSTRACT Quinoa is a highly adaptable crop due to its considerable genetic variability, making it an important trait for cultivation under different soil and climatic conditions. To achieve crop-wide adaptation, it is essential to identify variability based on morpho-agronomic differences and genotype x environment (GxE) interaction. This study aimed to characterize eight quinoa progenies in Brazil and Colombia. The experiments in Brazil were conducted in an irrigated area of the Fazenda Água Limpa, Universidade de Brasília, at 1,100 m, on two dates: March to July 2018 and May to August 2019. In Colombia, experiments were carried out in Santander de Quilichao and Popayán at 1,100 and 1,800 m, respectively. The treatments consisted of five progenies selected in Brazil, one from Colombia, and two from Ecuador. The experiments followed a complete randomized block design, with eight progenies and four replications. For statistical analysis, the F test was used with p≤0.01 and p≤0.05. Means were grouped by the Scott-Knott test. The AMMI (Additive Main effects and Multiplicative Interaction) analysis was performed, combining analysis of variance and analysis of principal components, to adjust the main effects of genotypes (G) and environments (E) and the GxE interaction. Significant differences were found at p≤0.01 and p≤0.05 for environments, genotypes, and the interaction of GxE. The progenies with wide adaptation to environments were BRX2, BRX5, BRX6 (selected from BRS Syetetuba) and PRIX (selected from Piartal), with average yields above 3,151.95 kg/ha. The genotypes showed differences at the same location in different periods, expressing the need to carry out selection for specific periods and locations. Genotypes BRX5 and BRX6 showed high agronomic potential in all evaluated environments, being promising for future genetic improvement programs.

Analysis of genotype by environmental interaction of common wheat (Triticum Aestivum L.) by non-parametric stability methods

Data from the Multi Environmental Field Trail (MET), which examined 24 varieties of common wheat, were divided into three "datasets" related to three of the five study locations. Using meta-analysis, these three data sets were compared with those from the whole experiment. The aim of the study is to determine whether a 4-year growing period in three country-specific locations, it is possible to establish a significant impact of the environment on the stability of a group of varieties. The analysis of the genotype x environment interaction (GEI) was performed in parallel in the three groups of data, which were compared with the entire MET database. A direct comparison was made on them regarding the possibilities of non-parametric methods to assess the stability of the variety. The analysis of the results of the four "datasets" is done through a number of statistical approaches, allowing them to be correctly compared at different levels. Genotype x environment interaction was found in each of the studied locations. The variation in yield in them is a result of the direct effect of the "year" and the combined effect of the genotype x year. At all three locations, the GEI is broken down into four main components, which is evidence of the strong linear and non-linear nature of the dispersion of grain yield. These results are a prerequisite for an objective assessment of genotype stability. All applied parameters give completely similar stability information for each of them, regardless of the test location. Data from one location are sufficient to assess the stability of each variety in a group. This may be the case if significant differences between the seasons of the trial, are found. The applied non-parametric methods for stability assessment give correct information about the varieties, in the presence of GEI, regardless of the conditions from which the data for analysis are selected.

Yield Performance and Stability Analysis of Some Fonio (Digitaria exilis) lines in Nigeria

The experiment to identify some fonio pure lines that are of superior agronomic traits, expressing high yield, stable and well adapted across some ecologies in Nigeria was under taken for D. exilis species. The Twelve (12) pure lines and a local check were evaluated in Nine (9) locations for two years. Combined Analysis and Stability Models were employed. The genotype EXPL03-10-01 had the tallest plants in the population, recorded the highest yield of 810.30kg/ha at Ryom and an overall combined yield of 696.09kg/ha. Finlay-Wilkinson stability model identified the genotype EXPL03-10-01 in the presence of changes in environmental quality, genotype x environment interaction (GEI) shows 30% higher than 1 in 2019 and 32% less than 1 in 2020 for slopes value having significant t-values in both years. GGE biplot analysis of grain yield indicated that, PC1 and PC2 explained a total of 66.3% and 15% of GGE sum of observed variations respectively. AMMI model deployed revealed that greater portion of total variability in the grain yield of the Fonio lines was contributed by the environments. G1 (EXPL03-10-01) was not only an ideal genotype in majority of the tested environments but was also found to be more stable than other genotypes according to GGE Biplot.

ASSESSMENT OF GRAIN YIELD STABILITY AND RELIABILITY IN SOME WINTER WHEAT VARIETIES

Considering the continued and unpredictable variation of climatic condition, the yield stability has become an important topic in wheat breeding. This study evaluated the grain yield of 11 winter wheat varieties with different genetic and ecologic origin during three years, to compare the effects of genotype, year and genotype × year interactions and to determine their stability and reliability for cultivar recommendations. The climatic conditions during the three years had the highest contribution (80.03%) to the yield variability of wheat, while the varieties had a lower influence (11.41%), and the variety x year interaction contributed only with 8.56% to the total variation. In 2020, the climatic conditions were significantly more favorable than in 2018–2019, allowing obtaining yield increases of 33.82-44.20%. Also, in 2018 there was a higher favorability of the growing conditions compared to that of 2019, associated with significant yield increases of 7.75%. The highest yield stability was observed in Boema and Galio varieties, on the background of a non-crossover genotype x environment (GE) interaction. The stability of these varieties has been associated with different yield levels, above the experience mean in Galio and below the experience mean in Boema, respectively. In Akteur and Josef varieties, the yield was strongly influenced by the GE interaction, being associated with lower levels than the experience mean. On the background of high occurrence probabilities (0.75–0.95) of unfavorable environmental conditions for wheat crop, Galio and Laurenzio varieties would have the highest yield reliability, associated with values of 5173–6328 kg/ha (Galio) and 5101–5979 kg/ha (Laurenzio). As such, given the results of all stability parameters, it turns out that the Romanian varieties showed a good adaptation to the local ecological conditions compared to most of the foreign varieties.

Weak genotype x environment interaction suggests that measuring scrotal circumference at 12 and 18 mo of age is helpful to select precocious Brahman cattle.

The aim of this study was to evaluate the genotype x environment interaction (GxE) for scrotal circumference (SC) measured at different ages using pedigree-based (A-1) and pedigree and genomic-based (H-1) relationship matrices. Data from 1,515 Brahman bulls, from the Cooperative Research Centre for Beef Genetic Technologies (Beef CRC) experimental dataset, were used in this study. SC was adjusted to age and body weight measured at 6 mo (SC6), 12 mo (SC12), 18 mo (SC18), and 24 mo of age (SC24). Body weight (BW) measured at 6 mo (BW6), 12 mo (BW12), 18 mo (BW18), and 24 mo of age (BW24) was used as criteria to describe the environment for SC in each age. All the animals measured were genotyped using medium-density SNP chips ("50k" or "70k" SNP) and their genotype were imputed using a reference panel with 729,068 SNP. The environment gradient (EG) was obtained by standardizing the solutions of the contemporary groups obtained by Animal Model with BW as the dependent variable. Then, the reaction norms (RN) were determined through a Random Regression Model. The breeding values (EBV) were estimated using either A-1 or H-1. The rank correlation was obtained using Spearman's correlation among the EBV estimated for the traits in analysis. For SC6 and SC24, higher estimates of heritability (h²) were obtained using A-1, when compared with those observed with H-1. In those ages, the improvement of the environment decreases the h² coefficient. On the other hand, the h² for SC12 and SC18 increased as the environment became more favorable, regardless of the matrix used. The RN for SC6 and SC24 estimated using A-1 and H-1 showed a decrease of variance from the worst to the best environment, an indication of existence of GxE. On the other hand, for SC12 and SC18, there were no significant differences between the EBV estimated in the lower and in the higher environments, regardless of the kinship matrix used, suggesting absence of GxE on those ages. Spearman's correlation among EBV estimated using A-1 and H-1 in different EG was practically equal to unity for all traits evaluated. In our study, there was weak evidence of GxE effect on SC in ages suitable for selection for sexual precocity. So, the absence of GxE at 12 and 18 mo means that these ages are advantageous for measuring SC to selection for sexual precocity. The advantage is that no changes in classification were observed when the sires were evaluated in different environments.

Assessing various environmental descriptors with respect to genotype x environment interaction for milk production traits in Bavarian Fleckvieh cattle.

Genotype x environment (GxE) interaction for production traits in Fleckvieh cattle was assessed by means of various environmental descriptors (EDs). It was also of particular interest to search for EDs useful for studying differing robustness or resilience of individuals which implies reasonable GxE interaction. The set of studied EDs included farm/herd environment (e.g. herd size, housing/feeding regimes, herd production level), geographical location (e.g. height above sea level), temperature humidity index and fat-to-protein ratio. Milk, fat and protein yield deviations for the first test day, the first 100 days and the 305-day-yield of the first lactation were used as phenotypes. Genetic correlations were estimated with bi- and multivariate sire models in case of categorical EDs and reaction norm sire models in case of continuous EDs. Further, rank correlations of bulls' estimated breeding values were calculated for different environmental levels/classes. Almost all estimated genetic correlations were significantly different from 1, but not <0.93. There were some exceptions for extremely different classes/levels of some EDs (e.g. average herd-year production, organic vs conventional farm systems, way of concentrated feed supply). Rank correlations were rarely below 0.95. In summary, no substantial GxE interactions for milk production traits were found with the studied EDs.

Agronomic Performance of Broomrape Resistant and Susceptible Faba Bean Accession

The faba bean (Vicia faba) is a temperate grain legume, that is regaining interest due to the high demand for food and feed uses and the environmental services provided. The parasitic weed broomrape (Orobanche crenata) appears as the major constraint to agricultural production in the Mediterranean Basin. The yield stability can be managed by adjusting agronomic practices and breeding for adaptation. In this study, we compared the performance of three susceptible faba bean accessions with that of eight lines previously selected for their broomrape resistance, in multi-environment field trials. Results confirmed that the grain yield in the region was negatively affected, mainly by broomrape infection, followed at a distance by ascochyta blight (Ascochyta fabae), whereas the grain yield was little affected by the low occurring levels of chocolate spot infection (Botrytis fabae). The yield was favored by rain at flowering and was reduced by low temperatures at pre-flowering and flowering, and by high temperatures at flowering and grain-filling. The combined ANOVA showed significant effects of the genotype, environment, and genotypex environment interaction. The weighted average of the absolute scores biplot (WAASB), a heat map with 21 scenarios based on the WAASB ratio and the multi-trait stability index (MTSI) were utilized to determine the mean performance and stability of the faba bean genotypes. Quijote, Navio6, Baraca and FaraonSC are proposed as ideal lines for cultivation in the region and to be further used in future breeding programs.

Higher Order AMMI (HO-AMMI) analysis: A novel stability model to study genotype-location interactions

Additive main effects and multiplicative interaction (AMMI) model is most widely used to analyze genotype x environment interactions (GEI) wherein interaction effects of location is masked by year effect. Hence, presently available models are not able to estimate interaction effects of genotype x location (GLI) and genotype x year (GYI) separately. Moreover, genotype ranking differs as number of years of evaluation vary making selection of genotype for target location difficult. In the present study, a novel stability model i.e., Higher-order-AMMI (HO-AMMI) analysis which can calculate GLI without the confounding effect of GYI and GLYI has been proposed. GEI of AMMI model and all 2-way interactions of HO-AMMI model follow χ2 distribution, whereas 3-way interaction (GLYI) of HO-AMMI follow noncentral χ2 distribution. With increase in number of years of evaluation contribution of GLI towards total variation increased whereas in AMMI model contribution of GEI towards total variation decreased. Variation explained by multiplicative components is higher in HO-AMMI compared to AMMI model. Genotypes were ranked using GL, GY and GL+GY+GLY interactions of HO-AMMI and GEI of AMMI for stability and yield and compared their ranks with field ranking. Correlation and linear regression analysis have indicated high association of GLI (HO-AMMI) with field ranking with high R2 values. Further, HO-AMMI model was able to remove the confounding effect of GYI and GLYI on GLI for accurate identification of genotype for target location irrespective of number of years of evaluation. Hence, HO-AMMI model can be used under multi-environment trials(MET) for selecting genotypes efficiently.