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Recommended List (RL) is the UK plant variety recommendation system introduced in 1944 for supporting growers in making decisions on variety choices. The current RL system is heavily focused on single-trial analyses developed in the 1980s without making full use of information across varieties and trial sites. Given the statistical advances that have been developed and adopted elsewhere, it is timely to review and update the methods for data analysis in RL. In addition, threats from climate change challenge the prediction of variety performance in future environments. Better variety recommendations, particularly for matching varieties to specific environments can be achieved through the improved modeling of effects from genetics, environments, and genetic-by-environment interactions. Here, we evaluate grain yield data from 153 spring barley varieties that were trialed for RL from 2002 to 2019. Our results show that the current RL system produces poor and inconsistent predictions on variety performance across environments. Improvement in RL can be achieved by using mixed models that account for genetic relationships among varieties, and additional improvement is possible if genetic-by-environment interaction can be modeled accurately. We highlight the relevance and importance of genomics in both variety registration and recommendation. [ABSTRACT FROM AUTHOR]

Diverse rice germplasm comprising 112 genotypes was evaluated for yield traits across three environments. Pooled and environmentwise analysis of variance revealed heterogeneity in the data and significant environment interactions for all the yield traits. As per AMMI (additive main effects and multiplicative interaction) and GGE (genotype and genotype x environment interaction) biplots, the influence of environment was significant and varying on all the component yield traits including grain yield and was not significant in case of flowering date. Dry season at Maruteru in 2014–15 (E1) was the most discriminative and representative environment for favourable plant growth in terms of plant height, panicle number and panicle length. None of the environments represented ideal environment for the favourable expression of grain number while all the environments were equally informative for thousand grain weight and grain yield. Panicle number, grain number and thousand grain weight were contributing to grain yield across the environments. Three genotypes Panthdhan 12, Konark and Udaygiri were the most stable genotypes for grain yield with favourable combination of associated yield genes for all the traits, viz. 1000 grain weight, the number of grains per panicle, the number of filled grains per panicle, productive tillers and plant height with higher yield, and grouped in one cluster. Genotyping using previously reported markers revealed that favourable alleles of yield genes associated with the number of productive tillers were predominantly found followed by alleles for the number of grains/filled grains per panicle correlating with the superior phenotypic value of the respective trait. The information on association of yield stability with reported yield genes from this study is useful in marker-assisted breeding studies for yield improvement and can be confirmed with various sets of genotypes under multi-environment testing. The identified superior genotypes are potential components in future breeding programmes and the development of stable adaptable varieties. The present study suggests that yield stability could be effectively achieved with targeted improvement of component yield traits associated with favourable alleles. [ABSTRACT FROM AUTHOR]

Maize is the staple food crop for millions of people in sub-Saharan Africa. Iron (Fe) and zinc (Zn) deficiency is a significant health risk that mainly affects low-income populations who rely solely on maize-based diets. This problem can be alleviated by developing micronutrient-rich maize grain. The aim of this study was to determine the adaptation and performance of hybrids for Fe and Zn concentration and grain yield under low soil nitrogen (N) and optimal conditions. Eighteen hybrids derived from lines and testers with low, medium, and high Fe and Zn concentration were grown during the summer rainy seasons of 2017 and 2018 at three locations under low and optimal N conditions. There were significant genotype and environment effects for grain yield, and Fe and Zn concentration, but the genotype by environment interaction effects were the largest, accounting for between 36% and 56% of variation under low N conditions. Low N levels significantly reduced grain yield, and Fe and Zn concentration. Hybrids G1, G2, G4, G7, G10, G11, and G16 were relatively stable, with relatively high mean Fe and Zn concentrations, and low additive main effects and multiplicative interaction (AMMI) stability values and iron stability index (FSI) and zinc stability index (ZSI) under low N conditions. These genotypes can be considered for production under low N stress conditions. Two environments (E4 and E3) were identified for good discriminatory power for genotype performance in terms of Fe and Zn content, respectively. Stable and high-yielding genotypes with high Fe and Zn concentration can be used as biofortified hybrids, which can contribute to a sustainable solution to malnutrition in the region, especially under low N conditions. [ABSTRACT FROM AUTHOR]

Twenty three advanced bread wheat genotypes have been evaluated against two released bread wheat varieties in 2018-19 and 2019-20 in nine diverse environments of Ethiopia. The experiment was laid out using alpha lattice design with three replications. Ten stability models were employed in order to assess stability and performance of 23 advanced bread wheat genotypes. Combined analysis of variance for grain yield has revealed that the environments, the genotypes and GEI effects were significantly different (p<0.001). In the present study, Environments, GEI and Genotypic effects accounted for 88.6%, 8.3% and 3.1% of the total grain yield variation, respectively. Twelve bread wheat genotypes, ETBW 9136, ETBW 9139, ETBW 9065, ETBW 9080, ETBW 9172, ETBW 9396, ETBW 9452, ETBW 9641, ETBW 9642, ETBW 9646, ETBW 9647 and ETBW 9648 produced grain yield that raged from 5.4 to 5.8 t ha-1, indicating their superior yielding potential. ETBW 9136, ETBW 9139, ETBW 9172, ETBW 9396, ETBW 9452, ETBW 9641, ETBW 9642 and ETBW 9646 were the most stable bread wheat genotypes as confirmed by five to ten stability models. However, ETBW 9452, ETBW 9641, ETBW 9642, ETBW 9646, ETBW 9647 and ETBW 9648 were susceptible to either stem rust or yellow rust or both. Providentially, ETBW 9136, ETBW 9139, ETBW 9172 and ETBW 9396 were superior yielding, stable, resistant and moderately resistant to wheat rusts. Thus, these four genotypes were the most promising advanced bread wheat genotypes to be verified and released in low to mid altitude areas of Ethiopia. [ABSTRACT FROM AUTHOR]

Wheat is one of the products with the widest cultivation area and adaptation ability in the world. Although the heritability of the variety is important in grain yield, the environment and genotype x environment interaction is significantly effective. This study was carried out in 6 different environments. According to the Additive Main Effect and Multiplication Interaction (AMMI) model; genotype, environment and genotype x environment interaction were found to be effective on grain yield, respectively, by 9.40%, 56.79% and 16.28%. It has been determined that, there is a positive relationship between grain yield and the number of spike per square meter and the number of grain in each spike. In the study, 3 mega environments were formed. G19 in the first mega environment, G13 in the second mega environment and G21, G22 in the third mega environment came to the fore. G9, G13 and G19 were seen as hopeful lines in the grain yield. Also, E5 was the ideal environment and, G19 had the highest adaptability and grain yield. It has been concluded that G19 may be a candidate for variety because genotypes that are stable in different environmental conditions are preferred by the producers. [ABSTRACT FROM AUTHOR]

Increasing the maize production capacity to ensure food security is still the primary goal of global maize planting. The purpose of this study was to evaluate genotypes with high yield and stability in summer maize hybrids grown in the Huanghuaihai region of China using additive main effects and multiplicative interaction (AMMI) analysis and best linear unbiased prediction (BLUP) technique. A total of 18 summer maize hybrids with one check hybrid were used for this study using a randomized complete block design (RCBD) with three replicates at 74 locations during two consecutive years (2018–2019). A three-way analysis of variance (ANOVA) and an AMMI analysis showed that genotype (G), environment (E), year (Y) and their interactions were highly significant (p < 0.001) except G × E × Y for all evaluated traits viz., grain yield (GY), ear length (EL), hundred seed weight (HSW) and E × Y for hundred seed weight. The first seven interaction principal components (IPCs) were highly significant and explained 81.74% of the genotype by environment interaction (GEI). By comparing different models, the best linear unbiased prediction (BLUP) was considered the best model for data analysis in this study. The combination of AMMI model and BLUP technology to use the WAASB (weighted average of absolute scores from the singular value decomposition of the matrix of BLUP for GEI effects generated by linear mixed model) index was considered promising for similar research in the future. Genotypes H321 and Y23 had high yield and good stability, and could be used as new potential genetic resources for improving and stabilizing grain yield in maize breeding practices in the Huanghuaihai region of China. Genotypes H9, H168, Q218, Y303 and L5 had narrow adaptability and only apply to specific areas. The check genotype Z958 had good adaptability in most environments due to its good stability, but it also needs the potential to increase grain yield. Significant positive correlations were also found between the tested agronomic traits. [ABSTRACT FROM AUTHOR]

The identification of high-yielding and stable genotypes for cultivation across differential production regions is among the key breeding objectives in cowpea improvement programs. This study was aimed to determine genotype-by-environment interaction (GEI) for grain yield to select high-yielding and stable cowpea genotypes for production in South Africa and identical agro-ecologies, and for cultivar development. Fifty cowpea genotypes were tested for grain yield across seven environments of South Africa using a 10 × 5 alpha lattice design replicated three times, during the 2019/2020 and 2020/2021 planting seasons. Grain yield data were subjected to analysis of variance (ANOVA), additive main effects and multiplicative interaction (AMMI) and the genotype-by-environment interaction (GGE) biplot analyses. ANOVA and AMMI showed significant genotype, environment and GEI effects. High grain yield was recorded for genotypes G35 (0.47 t ha−1), G1 (0.45 t ha−1) and G47 (0.43 t ha−1) across test environments. AMMI stability values (ASV); identified Acc-Cowp44 as the most stable genotype across all sites, recording the lowest ASV of 0.03. The comparison view of GGE biplot revealed Acc-Cowp29, Acc-Cowp38 and Acc-Cowp5 as ideal genotypes, possessing high grain yield of 0.19, 0.47 and 0.36 t ha−1, respectively. The identified genotypes are recommended for production and inclusion in subsequent breeding activities. [ABSTRACT FROM AUTHOR]

Multi-environment trials were carried out at 11 locations in different wheat growing zones of Ethiopia during 2017-18 and 2018-19 to identify high yielding, stable, biotic and abiotic stresses resistant varieties with improved quality traits for commercial release. Twenty-eight advanced bread wheat genotypes have been evaluated against two released bread wheat varieties. The experiment was laid out using alpha lattice design with three replications. Nine stability models were employed in order to assess stability and performance of 28 advanced bread wheat genotypes across 18 diverse environments. Combined analysis of variance for grain yield has revealed that the environments, the genotypes and GEI effects were significantly different (p<0.001). Environments, GEI and Genotypic effects accounted for 71.99%, 22.97% and 5.03% of the total grain yield variation, respectively. Significant GEI showed variable performance of genotypes across environments. Eight advanced bread wheat genotypes namely ETBW8595, ETBW8668, ETBW8751, ETBW8991, ETBW8996, ETBW9547, ETBW9553 and ETBW9554 produced grain yield of more than 5.0 t ha-1, indicating their superior yielding potential. ETBW8595, ETBW8668, ETBW8751, ETBW8991 and ETBW9554 were found the most stable bread wheat genotypes as confirmed by five to eight stability models. ETBW8751, ETBW8991 and ETBW9554 were highest yielding, stable, adaptable, resistant and moderately resistant to prevailing stem and yellow rust diseases. Thus, these three genotypes were the most promising advanced bread wheat genotypes to be verified and released in Ethiopia. These promising bread wheat genotypes can be included in multipurpose bread wheat crossing blocks in order to correct shortcomings of commercial varieties. [ABSTRACT FROM AUTHOR]

A critical comparison of stability models is essential to give an insight to breeders for developing relatively stable wheat cultivars. A multi-environment trial (MET) was conducted to assess the pattern of genotype by environment interaction (GEI) effects on yield using multiple stability models viz. Additive Main effect and Multiplicative Interaction (AMMI), GGE biplot analysis and stability parameters. Eighty-one wheat genotypes were evaluated during three consecutive years (2013/14, 2014/15 and 2015/16) across nine environments (sites x year combination) in Khyber Pakhtunkhwa, Pakistan. Graphical stability approaches such as AMMI and GGE provided almost similar results for identifying the high-yielding and stable wheat genotypes. The AMMI analysis identified G-58 and G-79, whereas GGE biplot identidified G-79 as the most stable and high yielding genotype. Numerical stability parameters like Eberhart and Russell's model and Francis coefficient of variation (CV) declared G-79 as top-ranked genotype while Shukla stability value (σi²) and Wrick' s ecovalence (Wi) identified G-80 (check cultivar Janbaz), G-52 and G-79 as leading wheat genotypes based on grain yield. Spearman's rank correlation revealed significant positive correlations of AMMI stability value (ASV) with CV, σi² with Wi, and Wi with CV indicating that these parameters could invariably be used for identifying stable wheat genotypes depending upon the nature of the experiment, breeding material, and the complexity of data. Different stability models identified G-79 as high yielding and stable genotype and thus could be recommended for commercialization in the province of Khyber Pakhtunkhwa, Pakistan. Furthermore, stability parameters can supplement the use of AMMI and GGE biplot analysis to get more credible and reliable scrutiny of wheat genotypes in METs. [ABSTRACT FROM AUTHOR]

Genotype by environment (GE) interaction complicates selection process of desirable genotypes. Therefore, newly developed germplasm or genotypes needs to be evaluated in multi-environment trials (METs) to know yield potential and adaptability. This experiment was designed to interpret GE interaction using 55 F5:8 bread wheat recombinant inbred lines (RILs) with five checks. Experimental material was planted using alpha lattice design in two replicates at Peshawar (E1and E5), Kohat (E2 and E6), Sarai Naurang (E3 and E7) and Dera Ismail Khan (E4 and E8) (Khyber Pakhtunkhwa) during 2014/15 and 2015/16. Locations in each year were considered as independent environments. Pooled ANOVA revealed significant interaction due to GE for days to heading, days to maturity, grain filling duration, grain growth rate, grains spike-1, 1000-grain weight and grain yield. Averaged over eight environments, wheat RIL's 44, 55, and 25 were desirable for days to heading, days to maturity, grain filling duration, grain growth rate, grains spike-1 and grain yield. Mean grain yield ranged from 1613 to 2471 kg ha-1 across environments. Among the tested environments, Sarai Naurang (E3 and E7) was identified as highly productive environment. The RIL25 had superior performance for various traits. Wheat RIL25 produced maximum grain yield in E2 (3123 kg ha-1), E3 (3665 kg ha-1) and E7 (3256 kg ha-1), thus emerged as promising line with wider adaptation. Conversely, RIL24 had higher grain yield in E1 (2800 kg ha-1); RIL1 in E4 (2884 kg ha-1); RIL58 in E5 (2370 kg ha-1); RIL7 in E6 (3200 kg ha-1) and RIL53 in E8 (2339 kg ha-1) suggesting their specific adaptability to respective environments. Grain yield was significantly correlated with days to heading, grain growth rate and grains spike-1. Keeping in view the significance of GE interaction, it would be worthwhile to carryout stability analysis for identifying high yielding RILs with suitable stability. [ABSTRACT FROM AUTHOR]

The development of stable and adaptable new cultivars are based only on positive results obtained from the interaction between the genotype and the environment. Therefore, the study aimed to test the stability and general adaptability of promising barley lines in terms of grain yield and traits in multi-environments. For this purpose, twelve barley genotypes were used in the study. The trials were carried out with four replications in a random design at seven environments in years 2012-13 and 2013- 14. The superior and stable genotypes were identified with GGE biplot and AMMI (Additive main effects and multiplicative interaction) models. The AMMI analysis showed that the major treatment sum of squares was affected by environments (80.6%), GE (14.0%) and genotypes (5.4%), respectively. On the other hand, the first two principal component axes (PCA 1 and PCA 2) contributed to the complete interaction with 88.1%, whereas, PCA 3 and PCA 4 axes only with 12.0%. The GGE biplot indicated that G4 is adaptable for all environments, while Altikat, G2 and G3 showed specific adaptation to E1, E3 and E5, G6, G7 and G8 to E6, respectively. According to both techniques, G2, G3, G6, G7, G8 and Altikat were the best genotypes with high yield, whereas G4 was the best with high yield, and stable and general adaptation. The results of biplot indicated that G4 (ARUPO /K8755//MORA/3/CERISE/SHYRI//ALEL I/4/CANELA/5/HART-BAR) was recommended for release and it was released as HEVSEL in 2017. On the other hand; G7 and G6 were protected as genetic material to use as parent in breeding programs for yield stability and quality respectively. [ABSTRACT FROM AUTHOR]

A field experiment was conducted during 2014 and 2015 using seven released triticale varieties in Wag-Lasta marginal high lands. Triticale (Triticosecale) is a hybrid of wheat (Triticum) and rye (Secale). The objective of the trial was to select adaptable and high yielding triticale variety for Wag-Lasta marginal high lands. Randomized complete block design with three replications and Plot size of 2.5 m length, 1.2 m width, 0.2 m row spaces were used. Phonological, agronomic and grain yield Data were taken. The combined analysis of variance showed a significant difference among the test varieties, location, and variety by location (GEI) interaction for most of the traits. Mean squares of Location was highly significantly (P < 0.01) different for all traits considered except spike length. Varieties were also highly significantly (P < 0.01) different from each other except biomass. Mean squares for variety by location interaction effects were highly significant (p < 0.01) for days to heading, days to maturity and significant (p < 0.05) for spike length and biomass; indicated the differential response of the varieties to test location for these traits. However, grain yield was not significantly affected by interaction effect that varieties were performed nearly similar in these test locations. Based on the data analysis and field evaluation variety Logawshibo followed by Dilfakare gave the highest grain yields of 30.32 qt/ha and 27.43 qt/ha, respectively. Thus, these varieties are recommended for Wag-Lasta marginal highlands and similar environment of the region. Moreover, these varieties should be demonstrated and popularized for the users. [ABSTRACT FROM AUTHOR]

Knowledge of genotype by environment interaction (GEI) is essential for identifying environment-specific and widely adapted genotypes. Multi-environment trials were conducted to evaluate the magnitude of genotype (G), environment (E) and GEI effects on yield and yield components of wheat. Eightyone wheat genotypes were evaluated under nine environments within Khyber Pakhtunkhwa, Pakistan during 2013/14, 2014/15 and 2015/16 cropping seasons. Combined analyses of variance for G and GEI were significant for almost all traits. Significant GEI suggested that the performance of genotypes was not consistent across environments, revealing strong influence of environments. Generally, GEI explained major portion of total variation and thus had larger effect than G and E on the expression of phenotype for all traits. Average over nine environments, genotype G-79 exhibited maximum values for tillers m-2 (181 tillers); grains spike-1 (71 grains); grain yield (4862 kg ha-1) and thus appeared as leading genotype for yield and production traits. Similarly, G-79 had higher grain yield in all test environments except E-03 i.e. E-01 (4537 kg ha-1); E-02 (4840 kg ha-1); E-04 (5035 kg ha-1); E-05 (4976 kg ha-1); E-06 (4797 kg ha-1); E-07 (5024 kg ha-1); E-08 (4767 kg ha-1); E-09 (4886 kg ha-1) signifying it as high yielding and widely adaptable genotype. However, G-08 with higher grain yield in E-01 (5272 kg ha-1) and E-07 (4840 kg ha-1), G-37 in E-04 (5106 kg ha-1) and E-08 (4906 kg ha-1), G-41 in E-05 (5034 kg ha-1) and E-06 (5272 kg ha-1), G-9 in E-02 (4975 kg ha-1) and G-49 in E-03 (5271 kg ha-1), established their specific adaptability in respective environments. Among environments, E-01, E-02, E-03, E-06 and E-07 were identified as highly productive environments in terms of grain yield. Correlation analysis revealed significant positive association of grain yield with tillers m-2 (rg = 0.72**), grains spike-1 (rg = 0.41**) and 1000-grain weight (rg = 0.30**). Based on mean yield performance, G-79 was found as high yielding genotype and thus could be recommended for commercialization in Khyber Pakhtunkhwa. [ABSTRACT FROM AUTHOR]

Knowledge of genotype by environment interaction (GEI) is essential for identifying environment- specific and widely adapted genotypes. Multi-environment trials were conducted to evaluate the magnitude of genotype (G), environment (E) and GEI effects on yield and yield components of wheat. Eightyone wheat genotypes were evaluated under nine environments within Khyber Pakhtunkhwa, Pakistan during 2013/14, 2014/15 and 2015/16 cropping seasons. Combined analyses of variance for G and GEI were significant for almost all traits. Significant GEI suggested that the performance of genotypes was not consistent across environments, revealing strong influence of environments. Generally, GEI explained major portion of total variation and thus had larger effect than G and E on the expression of phenotype for all traits. Average over nine environments, genotype G-79 exhibited maximum values for tillers m-2 (181 tillers); grains spike-1 (71 grains); grain yield (4862 kg ha-1) and thus appeared as leading genotype for yield and production traits. Similarly, G-79 had higher grain yield in all test environments except E-03 i.e. E-01 (4537 kg ha-1); E-02 (4840 kg ha-1); E-04 (5035 kg ha-1); E-05 (4976 kg ha-1); E-06 (4797 kg ha-1); E-07 (5024 kg ha-1); E-08 (4767 kg ha-1); E-09 (4886 kg ha-1) signifying it as high yielding and widely adaptable genotype. However, G-08 with higher grain yield in E-01 (5272 kg ha-1) and E-07 (4840 kg ha-1), G-37 in E-04 (5106 kg ha-1) and E-08 (4906 kg ha-1), G-41 in E-05 (5034 kg ha-1) and E-06 (5272 kg ha-1), G-9 in E-02 (4975 kg ha-1) and G-49 in E-03 (5271 kg ha-1), established their specific adaptability in respective environments. Among environments, E-01, E-02, E-03, E-06 and E-07 were identified as highly productive environments in terms of grain yield. Correlation analysis revealed significant positive association of grain yield with tillers m-2 (rg = 0.72**), grains spike-1 (rg = 0.41**) and 1000-grain weight (rg = 0.30**). Based on mean yield performance, G-79 was found as high yielding genotype and thus could be recommended for commercialization in Khyber Pakhtunkhwa. [ABSTRACT FROM AUTHOR]

Finger millet (Eleusine coracana (L.) Gaertn. subsp. coracana) production has become stagnant over the years and one of the possible ways to increase the production can be spread of widely adaptable high yielding cultivars. Five national finger millet cultivars were grown in randomized complete block design at ICARVivekananda Institute of Hill Agriculture for six consecutive years to evaluate the grain yield stability. The grain yield data were subjected to AMMI and GGE biplot techniques for assessing the stability and patterns of GE interaction in finger millet National cultivars. The combined ANOVA showed that finger millet grain yield was significantly affected by environment, which explained 54.67% of the total treatment (G+E+GE) variation, whereas the G and GEI accounted for 10.38% and 34.96%, respectively. The partitioning of GEI sum of squares using AMMI analysis indicated that the first two PCAs were highly significant. The first IPCA axis (IPCA1) accounted for 50.3% of the G×E interaction sum of squares. The second IPCA axis accounted for 38.2% of the interaction sum of squares. Both represented a total of 88.5% variation. AMMI 1 biplot indicated the general adaptation of genotype HR 374 across the environments, whereas the other genotypes showed specific adaptation to one or other environments. GGE-biplot graphical analysis further confirmed the results and revealed that HR 374 as an ideal genotype in terms of high yield and stability followed by RAU 8 as desirable genotype. In our research, both of AMMI and biplot models were successful in assessing the performance of genotypes and the selection of best genotype was identical in both of them. [ABSTRACT FROM AUTHOR]

The explication of candidate line and new varieties is uncomfortable the affect of genotype-by-environment interaction (GEI). The AMMI (Additive main effects and multiplicative interaction) analysis and GEI is making to estimation grain yield and understands GxE interaction patterns by researches as differential ranking of variety yields in multi-environment trials. So, a candidate of release and three spring barley varieties which registered in different years were reclaimed at seven environments. The experiments were performed according to a complete randomized block design with four replications. The stability and superiority of genotypes for yield was determined using AMMI and GGE biplot analysis. Factors (G, GE, and GEI) were found to be highly significant (P < 0.01) for grain yield. AMMI analysis indicated that the major contributions to treatment sum of squares were environments (79.4%), GE (13.9%) and genotypes (6.4%), respectively, suggesting that grain yield of genotypes were effected environmental conditions. The GGE biplot indicated that PCA 1 and PCA 2 axes (Principal component) were significant as P<0.01 and supplied to 86.3% of the complete GxE interaction. The GGE biplot indicated that four mega-environment were occured in terms of varieties. The varieties and candidate line took part in an independent mega-environment. On the other hand, Candidate line showed general adaptability (E1, E2, E4, E5 and E7), while Altikat and Sur 93 showed specific adaptation to E3 and E6 respectively. According to both techniques, especially Sahin 91, Sur 93 and Altikat came forward with low yielding, while candidate line with high yielding, stability and general adaptability. Statistical results indicated that AMMI and GGE biplot are informative techniques to compare varieties with candidate lines to discover general stability, adaptation pattern for practical recommendations. [ABSTRACT FROM AUTHOR]

Grass pea (Lathyrus sativus L.) is a crop used for both animal and human consumption. Recently, ICARDA has developed new grass pea lines with the objectives of improving its yield potential and adaptability. The objective of this study was to investigate grain and forage yields stability of several ICARDA released grass pea lines by using additive main effects and multiplicative interactions (AMMI) and site regression genotype plus genotype-by-environment interaction (SREG GGE) model. Fourteen grass pea advanced lines were evaluated at three research stations located in semi-warm regions in Iran, during 2005-08 crop seasons. The combined ANOVA showed that forage and grain yields were significantly affected by environment (E), genotype (G) and interactions of G×E. The partitioning of the sum of squares indicated that E accounted for 85% and 91% of the total (G+E+GEI) variation for forage and grain yields, respectively; whereas, G and GEI accounted for 4 and 11% for forage yield and 2 and 7% for grain yields, respectively. Using AMMI and GGE biplots, the lines identified as stable and high grain yielding were Sel.474, Sel.669, and Sel.686, while for forage yield the lines were Sel.474, Sel.678 and Sel.669. Kermanshah and Gachsaran were selected as favorable test environments for grass pea grain and forage yield ME trials, respectively. [ABSTRACT FROM AUTHOR]

The yield and protein performance in a soybean genotype result from its interaction with the prevailing environmental conditions. This makes selecting the best genotypes under varied target production environments more complex. This study's objectives were to determine protein content and protein stability of 30 elite soybean genotypes in major soybean-growing areas of Uganda, assess the yield performance and stability in soybeans and determine the relationship between the protein content and grain yield in soybeans. The genotypes were planted in a randomized complete block design of three replications for six seasons across eight locations in Uganda. Genotype and genotype-by-environment (GGE) biplot analyses classified the test locations into three mega-environments for soybean protein and grain yields. Genotype NII X GC 20.3 had the highest mean protein content of 43.0%, and BSPS 48A-9-2 and BSPS 48A-28 were superior for the mean grain yield (1207 kg ha−1). Bulindi was the most discriminating and representative test environment for soybean yield. A weak and negative correlation (r = −0.1**, d.f. = 29) was detected between the protein content (%) and yield (kg ha−1). The highest-yielding genotypes BSPS 48A-9-2, BSPS 48A-31, and Nam II × GC 44.2 are recommended for further evaluation under farmers' production conditions for selection and release as new soybean varieties in Uganda. [ABSTRACT FROM AUTHOR]