So far in this course we have dealt entirely with the evolution of characters that are controlled by simple Mendelian inheritance at a single locus. There are notes on the course website about gametic disequilibrium and how allele frequencies change at two loci simultaneously, but we didn’t discuss them. In every example we’ve considered we’ve imagined that we could understand something about evolution by examining the evolution of a single gene. That’s the domain of classical population genetics. For the next few weeks we’re going to be exploring a field that’s actually older than classical population genetics, although the approach we’ll be taking to it involves the use of population genetic machinery. If you know a little about the history of evolutionary biology, you may know that after the rediscovery of Mendel’s work in 1900 there was a heated debate between the “biometricians” (e.g., Galton and Pearson) and the “Mendelians” (e.g., de Vries, Correns, Bateson, and Morgan). Biometricians asserted that the really important variation in evolution didn’t follow Mendelian rules. Height, weight, skin color, and similar traits seemed to

Human biochemical genetics

In order to assess the genotype by environment interaction (GE) and select genotypes to exploit narrow and broad adaptations, twenty-two spring oilseed rape genotypes were subjected to field surveys at five experimental sites in the 2015-16 and 2016-17 growing seasons. Plant materials were sown in the form of a randomized complete block design with three replicates. The additive main effects and multiplicative interaction (AMMI) model was used to determine the genotype, environment, and GE effects. The sum of squares (SS) for the first three interaction principal components was very close to the SS for the GE signal; therefore, AMMI3 was diagnosed as the most accurate model to optimize predictive accuracy. SAN48 had the highest broad adaptability. In total, the chances of increasing yield were 55.80% from broad adaptations, 26.73% from narrow adaptations with 4 mega-environments, and an additional 17.47% from narrow adaptations with 5 mega-environments.

/pdf/analysis-of-genotype-environment-interaction-for-seed-yield-3w093laynp.pdf

Analysis of genotype × environment interaction for seed yield in spring oilseed rape using the AMMI model

Enhanced nodulation and higher germination using sludge ash as a carrier for biofertilizer production

This study was conducted to evaluate the yield components in mung bean [Vigna radiata (L.)Wilczek] using the correlation, path and factor analyses. It was found that there was considerable variation for the characters studied. Factor 1 composed of 100-grain weight, podlength, pod width, branches per plant and pods per plant. The grain weight was stronglycorrelated with pod length and pod width. Pods per plant were significantly and positivelyassociated with branches per plant. Factor 2 consisted of biological, straw and grainyields. The seed yield was highly associated with biological and straw yields. Factor 3comprised of seed per pod, pods and flowers per peduncle. The fourth factor was onlythe days to flowering. The last factor was plant height. The total factors had 74% ofthe total variance induced by the characters. It was firstly concludedthat the factor analysis together with path and correlationcoefficients could successfully be used for determining characters usable for selection in themung bean breeding programs

/pdf/yield-components-in-mung-bean-vigna-radiata-l-wilczek-2z8hm14r5z.pdf

YIELD COMPONENTS IN MUNG BEAN [Vigna radiata (L.) Wilczek]

GENOTYPE BY ENVIRONMENT INTERACTION ON YIELD AND MICRONUTRIENT CONCENTRATION OF BIOFORTIFIED COMMON BEAN (Phaseolus vulgaris L.) IN ETHIOPIA

Genetic diversity is essential for genetic improvement of given crops. If the information on genetic diversity is not enough to utilize, the available variability genetic diversity study should be crucial. Accordingly, twelve lentil germplasms were evaluated at two locations, Sinana and Agarfa, South eastern Ethiopia in the 2012/13 cropping season to obtain information on genetic diversity and variability in the Ethiopian lentil germplasm. At Sinana, the genotypic correlation revealed that number of pod per plant had positive and highly significant association with seed yield, whereas hundred seed weight, days to maturity, number of seeds/plant and plant height had positive but non-significant association with seed yield per plot. However, negative association was observed for stand percentage. At Agarfa, positive and highly significant association was observed between number of pods/plant and seed yield, whereas plant height and number of seeds/pod had negative and highly significant association with seed yield at the genotypic level. The genotypic path analysis at Sinana showed that number of pods/plant and seeds/pod had very high and positive direct effect on seed yield, whereas days to maturity and plant height had negative direct effect on seed yield. At Agarfa, positive direct effect on seed yield was observed in days to maturity and stand percentage; whereas negative direct effect on seed yield was observed in plant height and hundred seed weight at genotypic level.

	 

	Key words: Genetic diversity, correlation, direct and indirect effect.

/pdf/correlation-and-path-coefficient-analysis-of-yield-and-yield-1ltczzpvx5.pdf

Correlation and path coefficient analysis of yield and yield components in lentil (Lens culinaris Medik.) germplasm in the highlands of Bale, Ethiopia

Faba bean (Viciafaba L.) is widely cultivated in Ethiopia next to China. Faba bean (Viciafaba L.) is a partially Allogamous grain legumes grown for its high protein content in seed. The edible protein in the seed is used for human and animal consumption. The crop contributes to soil fertility through biological N-fixation. Though it is grows in many part of the country, the national average yield is very low due to multiple factors such as limited availability of stable high yielding and disease resistant cultivar. To this end, an experiment was conducted at Sinana, Agarfa, Adaba and Sinja for two years (2013-2014) to investigate the genotype x environment interaction and identify stable high yielding genotypes. Fifteen genotypes including the improved and local checks were grown in RCBD. Additive main effects and multiplicative interaction (AMMI) analysis was used to estimate genotype x environment interaction and found to be significant (P<0.01). The principal components (IPCA1) and (IPCA 2) explained 52.8% and 47.2% of the interaction, respectively. Mean grain yield of genotypes ranged from 2.6t/ha to 4.2t/ha with grand mean of 3.3t/ha. Most of the genotypes were highly responsive to the testing environment and adapted to more favorable environments. Genotype EH03014-1 and EKLS01013-1 gave higher grain yield than the checks, showed linear regression coefficient close to unity and deviation from regression close to zero. This implied that the two candidate genotypes showed stable performance across locations. Furthermore, EH03014-1 and EKLS01013-1 are resistant to major disease and showed20.41% and 14.58% grain yield advantage over the standard check, respectively. Based on their performance across locations, stability parameters, and their yield advantage over the checks, these two genotypes were selected as candidate varieties for verification in the coming bona 2016/17 cropping season.

https://article.sciencepublishinggroup.com/pdf/10.11648.j.plant.20170501.13.pdf

Genotypes by Environment Interaction of Faba Bean ( Viciafaba L .) Grain Yield in the Highland of Bale Zone, Southeastern Ethiopia

Twelve white common bean genotypes were evaluated along two checks at three diverse locations in the mid-altitude of Bale zone, southeastern Ethiopia for two consecutive years 2014 and 2015 in order to determine their stability. The genotype by environment interaction (GEI) has an influence on the selection and recommendation of cultivars. The objective of this work was to see the effect of GEI and evaluate the adaptability and stability of productivity of twelve white common bean genotypes using additive main effect and multiplicative interaction (AMMI) model.  The combined analysis of variance over locations revealed highly significant differences among the genotypes, locations and genotypes by location interaction. Among the 14 genotypes, the maximum grain yield over locations was obtained by genotype (G5) ICN Bunsi X S X B 405/5C-1C-1C-51 (2.05t/ha) followed by (G11) ICN Bunsi X S X B 405/7C-1C-1C-30 (1.96t/ha), and the site that gave the maximum grain yield was Ginir (2.16t/ha). The results of AMMI analysis indicated that the first four AMMI (AMMI-AMMI4) were highly significant (P<0.01). The GEI - was two times higher than that of the genotype effect, suggesting the possible existence of different environment groups. Based on the stability parameters like AMMI stability value (ASV), G12, G5, G7, G11, G3 and G13 were found to be as stable cultivars, respectively. As stability per se is not a desirable selection criterion and the most stable genotypes would not necessarily give the best yield performance, simultaneous consideration of grain yield and ASV in a single non-parametric index were also considered in identification of best varieties. Based on the Genotype Selection Index (GSI), which considers both the ASV and mean grain yield, genotype G5 and G11 were identified as stable genotypes for the study areas.

	Key words: AMMI Stability Value (ASV), Common bean, Genotype Selection Index (GSI), GE interactions

/pdf/evaluation-of-the-effect-of-genotype-environment-and-wns575w3xo.pdf

Evaluation of the effect of genotype, environment and genotype X environment interaction on white common bean varieties using additive main effect and multiplicative interaction (AMMI) analysis in the mid-altitude of Bale zone, Southeastern Ethiopia

The genotype x environment interaction (GEI) has an influence on the selection and recommendation of genotypes. To this end, G x E interaction and grain yield stability study was conducted for 17 advanced Ethiopian mustard across three districts (Sinana, Adaba and Agarfa) in the highlands of Bale zone during 2014 and 2015 main cropping season. Randomized complete block design with four replications was used. The combined analysis for the mean grain yield was highly significant (p≤0.01) for genotypes, environment and genotype x environment interaction. The mean seed yield of the locations was ranged from 0.9427 t ha-1 for Agarfa to 2.645 t ha-1 for Sinana in 2014. The Additive Main effects and Multiplicative Interaction (AMMI) analysis indicated that 76.7% of the GE sum squares was justified by the first two AMMI (AMMI1 and AMMI 2) components.  The regression coefficient (bi) of genotypes ranged from 0.629 to 1.345. Genotypes G7 was the most stable with optimum grain yield (2.21 t ha-1), bi- value nearer to unity (bi = 1.03) and minimum value of deviation from regression (0.12). Based on the AMMI Stability Value (ASV), G12, G10, G17, G5, G3, G2, G7, and G8 showed the lowest ASV indicating as they are most stable. However the most stable genotypes would not necessarily give the highest yield. Therefore, based on mean grain yield and the result of stability parameters such as ASV, bi and Genotypes Selection Index (GSI), genotype G7 was found the best candidate variety and recommended for possible release for the test environments and similar agro-ecologies.

	Key words: Additive Main effects and Multiplicative Interaction (AMMI), AMMI stability value (ASV), biplot, genotypes selection index (GSI), mustard, stability.

/pdf/genotypes-environment-interaction-analysis-for-ethiopian-18azk70pti.pdf

Genotypes × environment interaction analysis for Ethiopian mustard (Brassica carinata L.) genotypes using AMMI model.

Genotype x location interaction effects are of special interest for breeding programs to identify adaptation targets, adaptive traits and test sites. In order to identify stability and adaptability of small red bean cultivars sixteen genotypes were evaluated at the mid altitude of bale zone southeastern Ethiopia during main season 2015 and 2016. The cultivars were arranged in a randomized complete block design with three replications at each site of Goro, Ginir and Dellomena. The combined analysis of variance for mean grain yield revealed significant variation for genotypes, environment and GE interaction. The analysis of variance for the AMMI (Additive Main effects and Multiplicative Interaction) revealed that significant variation for genotypes, environment and GE interaction. From this analysis 42.53% was explained by AMMI 1 followed by AMMI 2 (28.29%), AMMI 3 (19.76%) and AMMI 4 (7.10%) of the interaction sum of squares. Therefore, the first two AMMI components justified 70.82% of the GE interaction sum of squares. The stability parameters regression coefficient (bi), deviation from regression analysis and ASV identifies G7, G6, G11, G1 and G12 showed the least value for ASV indicating as these genotypes showed stable performance over the sites. However stable cultivar wouldn’t necessarily gave the highest seed yield. Therefore based on Genotype Selection Index (GSI) which considers both the ASV and the mean yield, G8, G3, G6 and G7 were the most stable genotypes identified over the tested environments. Therefore, out of the tested genotypes, G8 and G3 were identified as stable cultivar to be selected for possible release during the coming cropping seasons.

/pdf/identification-of-stability-and-adaptability-of-small-red-305hhfn6mz.pdf

Gashaw Sefera

Papers

Correlation and path coefficient analysis of yield and yield components in lentil (Lens culinaris Medik.) germplasm in the highlands of Bale, Ethiopia

Genotypes by Environment Interaction of Faba Bean ( Viciafaba L .) Grain Yield in the Highland of Bale Zone, Southeastern Ethiopia

Evaluation of the effect of genotype, environment and genotype X environment interaction on white common bean varieties using additive main effect and multiplicative interaction (AMMI) analysis in the mid-altitude of Bale zone, Southeastern Ethiopia

Genotypes × environment interaction analysis for Ethiopian mustard (Brassica carinata L.) genotypes using AMMI model.

Identification of Stability and Adaptability of Small Red Bean Cultivars Using AMMI Analysis