Total Sum Of Squares Research Articles

English

The experiment, for oil content analysis, was conducted for two growing seasons under rain fed condition (2012-2013) in Humera and Dansha, and in a single year (2013 cropping season) in Sheraro (a total of five environments). The experiment, comprised of 13 sesame genotypes, laid out in randomized complete block design of three replications with the objective of determining the magnitude of Genotype × Environment Interaction (GEI) and oil content. There was highly significant (p<0.01) oil content variation based on genotypes, environments and GEI resulting 26, 42.7 and 30.9% of the total sum of squares for the oil content variation, respectively. The grand mean of the oil content was 53.9%, yielding genotypes G4 and G11 with the highest oil content (55.1%) each and G8 with the lowest oil content (51.4%). G4 was the exceptional genotype with highest oil content (55.1%) and oil yield (512.9 kg/ha). Environments, E4 and E5 were the favorable environments and E1, E2 and E3 were unfavorable environments for sesame oil production. According to the additive main effects and multiplicative interaction (AMMI1) bi-plot, genotypes G4, G13 and G10 were stable genotypes and genotypes G2, G8, G9, G3 and G1 were unstable genotypes in most of the environments. The AMMI 2 bi-plot showed that, genotypes G2, G3 and G9 were specifically adaptable genotypes and genotypes G10, G12, G4 and G7 were widely adaptable in most of the environments for their oil content. Oil content of sesame varies highly both across years and locations. Key words: Adaptable, additive main effects and multiplicative interaction (AMMI), bi-plot, environment, stability. &nbsp

Genotype X environment Interaction and Stability of Oil Content of Sesame (Sesamum indicum L.) Genotypes in Northern Ethiopia

The experiment for oil content analysis was conducted for two growing seasons under rainfed condition (2012-2013) in Humera and Dansha, and in a single year (2013 cropping season) in Sheraro (comprising a total of five environments). The experiment comprised of 13 sesame genotypes, laid out in a randomized complete block design of three replications with the objective of determining the magnitude of Genotype x Environment Interaction (GEI)and oil content. here was highly significant (p0.01) oil content variation based on genotypes, environments and GEI resulting 26%, 42.7 % and 30.9% of the total sum of squares for the oil content variation respectively. The mean of the oil content was 53.9%, with genotypes G4 and G11 with the highest oil content (55.1 %) each, and G8 with the lowest oil content (51.4 %). G4 was the exceptional genotype with the highest oil content (55.1 %) and oil yield (512.9 kg/ha). Based on the Environmental Index (EI) analysis, Environments E4 and E5 were the favourable environments, while E1, E2 and E3 were unfavourable environments for sesame oil production. According to the AMMI1(Additive main effects and multiplicative interaction) bi-plot, Genotypes G4, G13 and G10 with oil the content of 55.1%, 54.7 and 54.2 respectively, were stable genotypes. On the other hand, genotypes G2, G8, G9, G3 and G1 were unstable genotypes in most of the environments. The AMMI 2 bi-plot showed that, genotypes G2, G3 and G9, with long vector length, were specifically adaptable genotypes and genotypes G10, G12, G4 and G7 with shorter vector length were widely adaptable in most of the environments for their oil content. Oil content of sesame varies highly both across years and locations.

Current situation and influencing factors of humanistic care ability of clinical nurses

Objective To investigate the current situation of humanistic care ability of clinical nurses, and analyze its influencing factors. Methods A total of 311 clinical nurses involved in Anhui Province nursing special training were recruited in the study using cluster sampling in June 2017. General Information Questionnaire, Clinical Nurses' Humanistic Care Ability Questionnaire and Perceived Nursing Work Environment Scale in Chinese Vision (C-PNWE) were used for the investigation. Influencing factors were analyzed by multiple stepwise regression analysis. Results A total of 311 distributed questionnaires, 301 effective questionnaires were recovered. The total score of clinical nurses' humanistic care ability was (135.55±22.85); the total average score of C-PNWE was (2.87±0.37). Influencing factors of humanistic nursing ability of clinical nurses included professional practice, health care collaboration, working age, family relationship, and occupational interest, which can explain 20.6% of total sum of squares of humanistic nursing ability. Conclusions The humanistic nursing ability of clinical nurses is not optimistic. The development of professional practice ability, good medical cooperation, rich experiences, stable and harmonious family relationship, and the promotion of occupational interest are conducive to the improvement of clinical nurses' humanistic care ability. Key words: Nurses; Humanistic care; Influencing factors

ESTABILIDAD DEL RENDIMIENTO DE CRUZAS DIALÉLICAS ENTRE LÍNEAS DE MAÍZ DE ALTA Y BAJA APTITUD COMBINATORIA GENERAL

En los programas de mejoramiento genético enfocados a desarrollar híbridos de maíz (Zea mays L.), conocer la estabilidad del rendimiento de grano de líneas en combinaciones híbridas es transcendental. El objetivo del presente estudio fue evaluar la estabilidad del rendimiento de cruzas simples dialélicas provenientes de líneas de maíz de alta y baja aptitud combinatoria general (ACG). Se emplearon ocho líneas de la raza Chalqueño: cuatro de alta (A) y cuatro de baja (B) ACG, con diferente nivel endogámico. El rendimiento de mazorca por planta de 24 de las 28 cruzas simples posibles se evaluó en dos localidades del Estado de México, de 2011 a 2013; en las evaluaciones de 2012 y 2013 se incluyeron cuatro testigos comerciales. Se empleó el método AMMI y se encontraron diferencias significativas (P ≤ 0.01) entre ambientes, genotipos, interacción genotipo × ambiente y en los efectos del primer componente principal (CP1). La variabilidad total debida a interacción fue explicada por el CP1 (86.62 %) y CP2 (13.38 %). El modelo retuvo 86.69 % de la suma total de cuadrados. Las cruzas A × B expresaron los rendimientos más altos y las cruzas A × A la mayor estabilidad. La cruza A3 × B3 fue estable y con alto rendimiento (108.27 g/planta). La respuesta de las cruzas a un ambiente específico demostró que además de la ACG de las líneas, la adaptación y la adaptabilidad de la cruza juegan un papel importante para el desempeño de su rendimiento. Las mejores cruzas igualaron en rendimiento de grano al mejor testigo.

Predicting structural classes of proteins by incorporating their global and local physicochemical and conformational properties into general Chou's PseAAC

In this study, I introduce novel global and local 0D-protein descriptors based on a statistical quantity named Total Sum of Squares (TSS). This quantity represents the sum of the squares differences of amino acid properties from the arithmetic mean property. As an extension, the amino acid-types and amino acid-groups formalisms are used for describing zones of interest in proteins. To assess the effectiveness of the proposed descriptors, a Nearest Neighbor model for predicting the major four protein structural classes was built. This model has a success rate of 98.53% on the jackknife cross-validation test; this performance being superior to other reported methods despite the simplicity of the predictor. Additionally, this predictor has an average success rate of 98.35% in different cross-validation tests performed. A value of 0.98 for the Kappa statistic clearly discriminates this model from a random predictor. The results obtained by the Nearest Neighbor model demonstrated the ability of the proposed descriptors not only to reflect relevant biochemical information related to the structural classes of proteins but also to allow appropriate interpretability. It can thus be expected that the current method may play a supplementary role to other existing approaches for protein structural class prediction and other protein attributes.

English

Wheat is an important staple food for Ethiopian people, and its straw has been used as sources of feed for animals. Nine wheat genotypes introduced from International Center for Agricultural Research in the Dry Areas (ICARDA) were evaluated at Werer Agricultural Research Center from 2010/2011 to 2013/2014, at Gewane from 2011/2012 to 2012/2013 and at Waidulale in 2013/2014 cropping seasons using randomized complete block design (RCBD) in three replications with the objective of identifying wide adaptable and high yielding bread wheat varieties. The combined analysis of variance (ANOVA) showed that the effects of year (Y) and locations (L) were highly significant for all parameters studied. The combined ANOVA of grain yield (GY) revealed highly significant (P<0.01) G x Y interactions effects for almost all traits. The environment (E) effect accounted for 67.8% of total sum of squares (TSS) followed by the Genotype by Environment Interaction (GEI), which accounted for 8.2% and G (3.6%). The highest yielding genotypes selected from the National Variety trial (NVT) were Moontiji-3 (3488 kg/ha), Doukkala-4 (3258 kg/ha) and Saamid-3 (3158.00 kg/ha) with the maturity date of 82.2, 89.5 and 84.2 days, respectively. Generally, the yields of the three candidate varieties were higher and stable over years and across locations.     Key words: Genetic variability, genotype x environment interaction, yield and yield stability.

Robust modeling of heating and cooling loads using partial least squares towards efficient residential building design

Partial least squares method was used to model residential building heating and cooling loads. These loads were modeled as functions of eight input variables such as relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area and glazing area distribution. The data for the models were taken from the literature and they consisted of values obtained through a commercial software package. Model validation was performed using k-fold cross validation method. Model validation was also performed using an analysis of total sum of squares of the data explained by the partial least squares latent variables. Validated models were compared against ordinary least squares models for heating and cooling loads, respectively. These models were used to determine the most influential input variables so that efficient building designs can be made. The results indicated that it is feasible to apply partial least squares regression to heating and cooling loads; and significant reduction in dimensionality may be achieved using the importance information provided by this method.

English

This work investigated the effects of seven pollination bag treatments on three varieties of sorghum for: grain loss to birds; total weight of five panicles (g); total grain weight of five panicles (g); average grain weight per panicle (g); germination per cent; and occurrence of grain mold during 2016. Varieties were: 1167048 hybrid (brown seeded); BR007B (red seeded); and P9401 (white seeded). The bag treatments were: 1. No bagging; 2. Kraft paper; 3. Kraft paper + plastic bag screen; 4 Used duraweb® SG1; 5. Used duraweb® SG2; 6. New duraweb® SG1; 7. New duraweld® SG2. High bird pressure resulted in 100% seed loss on uncovered panicles and 75% under Kraft paper pollination bags. Birds preferred white seeded P9401, which led to no seed recovery under Kraft paper bags. There was virtually no bird damage with all other pollination bags. For panicle and grain yields the varieties performed in the order 1167048>BR007B>P9401. Unprotected panicles and paper bag treatments had the lowest yields. Panicles covered with the new synthetic bags exhibited 195 to 652% higher yields compared to Kraft paper bags. Varieties x bag type interactions were not important as they contributed 4 to 6% to the total sum of squares for yield traits. Germination test under normal and stress conditions showed no significant adverse effect of bag treatments on seed health. Reused bags performed as well as new bags for all of these traits. Varieties differed significantly for the occurrence of five grain mold pathogens, with highest occurrence of Alternaria, up to 40%, on 1167048 hybrid. Of the five pathogens, bag types differed significantly for Phoma with the highest occurrence of 9% on re-used duraweb®SG2 bags. Thus bags require disinfecting and cleaning before re-use. It is concluded that nonwoven synthetic bags are a better choice than the Kraft paper pollination bags for increasing the grain yield and virtually eliminating the bird damage in sorghum.    Key words: Sorghum, nonwoven fabrics, kraft paper, pollination bags, bird control, grain mold.

Agronomic Performance, Genotype X Environment Interaction and Stability of Black Cumin Genotypes Grown in Bale, Southeastern Ethiopia

Twenty Black Cumin genotypes were evaluated across locations to assess their agronomic performance, genotype by environment interaction and seed yield stability during 2011 and 2012 at Sinana, Goro and Ginir in randomized complete design with three replications. Except the number of capsules per plant, the analysis of variance indicated that there was highly significant variation (p<0.01) among the genotypes in days to flower, plant height, primary branches, days to maturity, biomass yield and seed yield. The combined analysis of variance indicated that the genotype x environment interaction was highly significant (p<0.01) indicating that there is a need to know which component of the interaction is contributing to the variation. The genotype by environment interaction explained contributed to the majority (79.16%) of the variation while the genotype and environment respectively explained 19.72 and 1.12% of the total sum of squares. The variation is majorly contributed by the genotype x environment interaction than genotype indicating that there was substantial difference in genotypic response across environments. AMMI stability analysis revealed that all the four AMMI components are highly significant. The first and second principal components contributed 70.85% and 17.0% of interaction sum of squares respectively. The PCA1 and PCA2 had sum of squares greater than that of genotypes and cumulatively contributed to 87.86% of the total GEI. Based on the calculated AMMI stability value, BC-DM-9 was the most stable genotypes followed by ACBC- 6 and AC-BC-19. On the other hand, genotype AC-BC-10 was the most unstable followed by MAB-057, ACBC- 8, Local and AC-BC-4. AMMI biplot of seed yield indicated that BC-DM-11 Xereta-1, AC-BC-9 and MAB-018 expressed a highly interactive behavior while genotype 394640-539 showed low interaction and thus stable in its seed yield (kg/ha).

Soil moisture induced genotype-by-environment interaction for root volume of upland rice

Sixteen rice genotypes comprising established cultivars, recent releases and breeding lines were established in the greenhouse under different moisture levels, obtained from a combination of the amount and number of times of moisture application, to study genotype-by-environment interaction (GEI) for root volume (RV), and also probe into the level of moisture imposition, that would be adequate for screening of genotypes for response to soil moisture stress. Across the simulated environments, W880-9-32-1-1-12-HB had the largest root volume of 8.71 cm3, whereas ITA 257 had the lowest (4.89 cm3). Genotype (G) accounted for significant (P &lt; 0.001) 10.6%, environment (E) (P &lt; 0.001) captured 79.0%, and GEI (P &lt; 0.001) 10.4% of the total sum of squares. The GGE biplot captured 82% of the G+GE and clustered the environments into two groups, with OS 6 being the best for RV in the rainfed environment (E10). W880-9-32-1-1- 12-HB recorded the best RV under environments with adequate to limited moisture, but was less stable, and recorded grain production (13.5 g/plant) close to the best mean of 16.0g/plant by ITA 150 and 14.1 g/plant by IRAT 170. Environments were generally positively correlated with vegetative and yield traits, but E2 (100% moisture requirement applied once in two weeks) was more representative of the screening condition while E10 (rainfed) was highly discriminating, and would be appropriate for discarding genotypes with poor RV. Overall, E1, E2, E4 and E7 were identified as moisture conditions that are appropriate for selection of genotypes for general adaptation for RV within the overall goal of developing drought tolerant rice.

Soil property predictors of soybean yield using yield contest sites

ABSTRACTState yield contests offer a unique opportunity to examine the high end of crop productivity. Yield-contest-entered and average-yielding areas on the same or a similar soil can provide large yield and soil property variations to better examine the relationships among various near-surface soil properties and soybean (Glycine max L. [Merr.]) yield. The objective of this study was to evaluate the relationships among a suite of near-surface soil properties and soybean yield across average- and high-yield areas using state yield-contest sites. Multiple regression analyses were conducted to evaluate best-fit relationships among various soil physical, chemical, and biological properties and yield separately for average- and high-yielding areas and for data combined across yield areas. Soybean yield variation was most explained for the high-yield-area dataset (R2 = 73%) and less explained for the average-yield-area (R2 = 51%) and the combined (R2 = 50%) datasets. Extractable soil Ca and S explained the largest proportion of yield variation (37% and 31% of total sum of squares) in the high-yield setting and both were inversely related to yield. A better understanding of the soil environment may be a key component of more frequent attainment of the 6270 kg ha−1 (100 bu acre−1) soybean yield mark. Additional soil properties, beyond those evaluated in this study, may need to be included for a more complete understanding of the soil environment that is associated with high-yield soybean production.

English

A grain yield trial was conducted in three locations in Southern Ghana. The objective of the trial was to determine the effects of genotype by environment interaction on 90 early maturing hybrid maize. The trial was conducted in Fumesua, Ejura and Kpeve; representing the forest, forest-Savanna transition and coastal-Savanna transition zones respectively. The analysis of variance for grain yield demonstrated that genotypic and environmental effects were highly significant (P&lt;0.001) while their interaction was significant (P&lt;0.005). The genotypes contributed 34.4% of total sum of square percentage while environment contributed 31.1% of the total sum of square of the variance. The genotype main effect plus genotype &times; environment interaction biplot explained 85.2% of total variation of the sum of squares for grain yield. P40, P16, P78, P53, P41 P9 and P3 were identified by GGE biplot analysis as high yielding and stable genotypes while P20, P80, P22, and P15 were low yielding but very stable genotypes. On the other hand, P59, P41, P16, P26 and P50 were high yielding but not stable and was recommended that genotypes with broad range adaptability can be tested on farmers field for possible release. &nbsp; Key words: Tropical maize early maturing (TZEI), Genotype &times; environmental interaction (GGE) biplot, hybrid, Fumesua, Ejura, Kpeve trial, environment, treatment.

Environment effects for earliness and grain yield traits in F1 diallel populations of maize (Zea mays L.).

Five maize inbred lines, 20 F1 diallel hybrids and two check genotypes were evaluated through genotype × environment interaction (GEI) and GGE biplot for earliness and yield traits at four locations. Genotype, environment and GEI showed highly significant differences for all the traits. In total sum of squares, environment and genotype played a primary role, followed by GEI. Larger effects of environment and genotype to total variation influence the earliness and yield traits. However, according to the GGE biplot, the first two principal components (PC1 and PC2) explained 95% of the variation caused by GEI. GGE biplot confirmed the differential response of genotypes across environments. F1 hybrid SWAJK-1 × FRHW-3 had better stability, with a good yield, and was considered an ideal genotype. F1 hybrid FRHW-2 × FRHW-1 showed more earliness at CCRI and Haripur, followed by PSEV3 × FRHW-2 and its reciprocal at Swat and Mansehra, respectively. F1 hybrids FRHW-1 × SWAJK-1, PSEV3 × SWAJK-1 and SWAJK-1 × FRHW-3 at Mansehra and Swat produced maximum grain yield, followed by SWAJK-1 × FRHW-1 and PSEV3 × FRHW-1 at Haripur and CCRI, respectively. Overall, maize genotypes showed early maturity in plain areas (CCRI and Haripur) but higher yield in hilly areas (Mansehra and Swat). © 2017 Society of Chemical Industry.

Performance evaluation of tomato accessions across two locations of Khyber Pakhtunkhawa

Identification of adaptable and stable genotypes is an important aspect of applied crop breeding which can insure sustainable crop production. Adaptability studies in tomato is an essential part of breeding for adequate evaluation of genotypes accomplished through environments (years/locations). A set of forty tomato accessions of diverse origin were tested at Swabi and Haripur locations to assess their performance. The experiment was carried out in randomized complete block design with three replications. Data were recorded on 50% flowering, days to fruiting and yield per hectare. Genotype (G), environment (E) and GEI share in total sum of squares were also studied. Significant (p≤0.01) differences were observed among the accessions, environments (locations) and G × E interaction means for majority traits, which assured greater genetic variability among the accessions due to their divergent genetic make-up, as well as environments. On average, the accessions showed best performance in Swabi location than Haripur for yield and duration parameters. Overall, the promising accessions could be further explored for improvement in tomato fruit yield. Keywords: Genotypes; Environments; Genotype-by-environment interaction http://dx.doi.org/10.19045/bspab.2017.60068

PERFORMANCE AND SURVIVAL OF PERENNIAL RICE DERIVATIVES (ORYZA SATIVA L./ORYZA LONGISTAMINATA) IN LAO PDR

SUMMARYGenotype by environment (G x E) interactions for grain yield were investigated in 13 perennial rice (Oryza sativa L./Oryza longistaminata) derivatives over three sites and 2 years in Lao PDR. Genotype accounted for 29.0% of the total sum of squares, with environment and the G x E interaction responsible for 60.2 and 10.8%, respectively. Cluster analysis identified three environment and six genotype groups, which accounted for 49.7, 98.0 and 42.8% of the E, G and G x E sums of squares, respectively. Principal component axes 1, 2 and 3 accounted for 54.0, 30.6 and 11.7% of the G x E sum of squares, respectively, with PCA1 indicating yield potential, PCA2 timing of cessation of rainfall in the 2011 wet season, and PCA3 environmental stresses affecting regrowth in the 2012 wet season. Genotype groups differed in adaptation to these contrasting conditions. G6 (Line 213, 240 and RD23) was widely adapted to all environments, with G5 (Line 248) being especially adapted to the 2012 environments. G3 and G4 were neutral, though G3 (Line 53) showed some preference for the Na Pok environments. G1 and G2 were poorly adapted everywhere, with the tall and late G1 (Line 11) being especially poor at Na Pok 2011 in a dry finish. While yields were lower in 2012, all derivatives survived the dry season with access to life-saving irrigation. This is promising, as the annual rice RD23 was unable to ratoon under these conditions, and had to be re-sown. Importantly, Line 213, 240 and 248 yielded comparably to RD23 from regrowth in 2012. Development of perennial rice should target rainfed and especially upland environments.

Proportional subclass numbers in two-factor ANOVA

ABSTRACTThe between-within split of total sum of squares in one-way analysis of variance (ANOVA) is intuitively appealing and computationally simple, whether balanced or not. In the balanced two-factor setting, the same heuristic and computations apply to analyse treatment sum of squares into main effects and interaction effects sums of squares. Accomplishing the same in unbalanced settings is more difficult, requiring development of tests of general linear hypotheses. However, textbooks treat unbalanced settings with proportional subclasss numbers (psn) as essentially equivalent to balanced settings. It is shown here that, while psn permit an ANOVA-like partition of sums of squares, test statistics for main effects of the two factors generally test the wrong hypotheses when the model includes interaction effects.

Field Design Factors Affecting the Precision of Ryegrass Forage Yield Estimation

Core Ideas The row‐column designs were 36% more efficient than the randomized complete block designs. The impact of block size and number of replicates on trial precision was dependent on block shape. Increasing number of replicates decreased LSD only in wider blocks compared to narrow blocks. Field‐based agronomic and genetic research relies heavily on data generated from field trials. Therefore, it is imperative to optimize the precision of yield estimates in cultivar evaluation trials. Experimental error in yield trials is sensitive to several factors, some of which are classified as experimental design factors (i.e., plot size, block size, and number of replicates). The objective of this study was to conduct a retrospective case‐study analysis of long‐term perennial ryegrass (Lolium perenne L.), Italian ryegrass [Lolium perenne L. ssp. multiflorum (Lam.) Husnot], and hybrid ryegrass (Lolium boucheanum) forage yield trials to quantify the effects of block size, block shape, plot size, and number of replicates on trial precision. A total of 142 forage yield trials sown 2001 to 2011 across five locations by the Department of Agriculture, Food and the Marine (DAFM), Ireland, were considered. The contribution of row and column sum of squares (SS) to total SS decreased with increasing block size from 6 (71% of total SS) to 33 cultivars per block (51% of total SS). Empirical estimates of LSD decreased with increasing number of replicates only for square blocks, but not for rectangular blocks. Across different block sizes from 6 to 42 plots per block, row‐column designs were 36% more efficient than complete blocks. Increasing plot size was consistently ineffective. Use of incomplete block designs would significantly improve precision for the DAFM trials. This case study provides a template by which any research with long‐term data can be used to design more efficient field trials.

Genotype by environment interactions for grain yield of perennial rice derivatives (Oryza sativa L./Oryza longistaminata) in southern China and Laos

Perennial grains have been proposed to stabilise fragile lands while contributing grain and grazing in mixed farming systems. Genotype by environment (GxE) interactions for grain yield were investigated in 22 perennial rice (Oryza sativa L./Oryza longistaminata) derivatives over four successive growing seasons at three sites in Yunnan in southern China and one site in Lao PDR. The GxE interaction accounted for 25.7% of the total sum of squares, with environment and genotype responsible for 57.4% and 16.9%, respectively. Cluster analysis identified seven environment and six genotype groups, which accounted for 55.6% of the GxE sum of squares. Principal component axes 1, 2 and 3 accounted for 42.3%, 19.1% and 16.5% of the GxE-SS, respectively, with PCA1 indicating yield potential, PCA2 delay in phenology under environmental stress, and PCA3 ratoon percentage. Environment groups differed in mean temperature, whether dry season or wet season, and occurrence of environmental stresses, such as periods of low minimum temperature or periods of rainfall deficit. Genotype groups differed in adaptation to these diverse environments. For genotype groups, G5 (PR23) was highest-yielding and broadly adapted across environments, while G1 (line 188, both 137s, both 139s, both 147s) was low-yielding and poorly adapted. Other genotype groups showed preferential adaptation: G3 (lines 60, 251, 264, Bt69, Bt71) to Simao/Dry Season (E3 and E4), G4 (lines 75, 243, 246, 249, 255) to Menglian/Wet Season (E1 and E2), G2 (line TZ) to Jing Hong 2013 (E7), and G6 (lines 56, 59, 214) to Jing Hong 2102 and Na Pok (E6 and E5). The results imply that regrowth success and maintenance of spikelet fertility over regrowth cycles are important for adaptation of perennial rice, especially to low minimum temperature at higher altitude and rainfall deficit at lower altitude, and future breeding programmes in perennial rice should address these environmental stresses. The high yield and broad adaptation of PR23 (G5) over environments makes it a prime candidate for release to stabilise fragile lands in the humid and subhumid tropics, while contributing grain and forage in mixed-farming systems.

AMMI Stability Analysis and Estimation of Genetic Parameters for Growth and Yield Components in Cassava in the Forest and Guinea Savannah Ecologies of Ghana

Twenty cassava genotypes were arranged in a randomised complete block design with three replications and assessed for growth and yield stability using the additive main effect and multiplicative interaction (AMMI) analysis. Highly significant (P&lt;0.001) effects of genotype, environment, and genotype ⁎ environment interaction were observed for all traits studied. The AMMI analysis of variance indicated that genotype accounted for 51% of the total sum of squares for height at first branching followed by environment (33%) and interaction (15%). For fresh root yield, environment effects accounted for 37% of the total sum of squares, whilst genotype and interaction accounted for 32% and 29%, respectively. Genotypic variances for harvest index (HI), plant height, storage root yield, and dry matter content contributed a greater proportion of the phenotypic variance indicating stronger genetic control. This suggests better chance of progress in the genetic improvement of these traits. Genotype MM96/1751 combined high yield with stability according to the yield stability index ranking across environments. On the other hand genotypes UCC 2001/449 and 96/1708 though high yielding were unstable according to AMMI stability value scores. However they can be tested further in more environments to ascertain their specific adaptability for release to farmers for cultivation to boost cassava production and ensure food security.

AMMI Model for Yield Stability Analysis of Linseed Genotypes for the Highlands of Bale, Ethiopia

In order to determine stable linseed genotypes with high grain yield, field experiments were conducted with 14 genotypes for two years (2014-2015) at three locations in the highlands of Bale zone, Ethiopia. The genotypes were laid out in randomized complete design with four replications in each environment. The objective of this study was to identify and recommend high yielder, stable genotypes for testing sites and similar agro-ecologies using the stability parameters. The combined analysis of variance showed highly significant differences for the genotypes, environment, and genotype by environment interaction indicating the possible existence of stable genotypes among the tested once. The results of AMMI (additive main effect and multiplicative interaction) analysis indicated that the first two AMMI (AMMI1-AMMI2) were highly significant (P<0.01). The partitioning of the total sum of square exhibited that the effect of environment was a predominant source of variation followed by genotypes and GE interaction effect. Based on the stability parameters regression coefficient, deviation from regression and mean grain yield out of the tested G6, G9, G11, and G8 were found to be stable. However, the AMMI Stability Value (ASV) discriminated genotypes G12, G4, G6, G13, and G9 as stable genotypes respectively. Based on the Genotypes Selection Index (GSI) the most stable genotypes with high grain yield were G6 and G9. Therefore these two genotypes were identified as candidate genotypes to be verified for possible release.