Total Sum Of Squares Research Articles

Genotype × environment interaction and stability analysis of advanced field pea (Pisum sativum L.) genotypes in Southeastern Ethiopia

Field pea is a leguminous crops that have a significant economic contributions to sustainable Ethiopian agriculture. The experiment was conducted at four location in 2015/16 to 2016/17 main cropping seasons with the objectives of to determine the magnitude of genotype by environment interactions and its stability of thirteen field pea genotypes using randomized complete block design with four replications. The AMMI analysis revealed that significant (p<0.01) differences between genotypes, environments and genotype by environment interactions for grain yield. This indicates the genotypes responded differently across environments. The highest mean grain yield was recorded from genotype G12 which records (2992 kg ha−1) followed by genotype G4 2974 kg ha−1 across environments. The AMMI analysis showed that genotypes, environments and there interaction accounted about 72.9 %, 1.2 % and 5.7 % of the total sum of squares respectively. The GGE biplot first two principal components explained about 57.42 % of the total sum square of GEI. The GGE biplot revealed that G4 and G12 were the most stable and high yielding genotypes, while G10 and G9 were the lowest yielder and stable genotypes. The results indicated that genotypes responded differently to environmental conditions and the environments influenced genotype performances.

Genotype by Environment Interaction Using AMMI, GGE Biplot and Multivariate Analysis of Nepalese Aromatic Rice Landraces

Aromatic group of rice comprises fine, medium, long and extra-long grain that has rich in agro-biodiversity, carries historical and cultural significance in Nepalese society. The objective was to identify stable, high-yielding aromatic rice landraces for Nepal Thirty aromatic rice landraces including two released varieties as standard checks were evaluated at three locations during 2020 and 2021. The experiment was laid out in a Randomized Complete Block Design with three replications. The combined analysis of variance indicated differences among genotypes and genotype-environment interactions for grain yield. The additive main effects and multiplicative interaction (AMMI) analysis revealed that genotypic effects for 69%, genotype × environment interaction effects for 28% and environmental effects accounted for 3% of the total sum of squares. Similarly, GGE biplot analysis showed that the principal components (PC1 and PC2) accounted for 53% and 47% of the GGE sum of squares, respectively. Based on AMMI1, AMMI2 biplots, and GGE biplots, the genotypes Samba Masuli Sub-1 and Sugandhit Dhan-1 were identified as the high yielding and stable fine and aromatic rice landraces. Other promising genotypes are Damari Dhan, Tilki, Hiupuri, Kalo Basmati, Gouriya, and Brahmamusi Dhan. Specifically, Samba Masuli Sub-1 demonstrated the highest grain yield performance at the NRRP, Hardinath, while Sunaulo Sugandha and Balamsari Dhan excelled in the Tarahara and Khajura environments, respectively. In PCA analysis, four PCs showed significant eigen value &gt;1.0 contributing more than 84% of total variance. Biplot analysis reflected that 1000 grain weight, panicle length, heading and maturity days, and grain yield showed greater variation among the landraces. The results provide a robust basis for the further evaluation and verification of this findings in advance stage trials. The superior and outstanding landraces with higher yields and aroma are potential future varieties that can be either use directly as varieties after selection or utilize in future breeding programs for the development of new aromatic rice varieties.

Combining ACE, PLS-R, and SVM-R for rapid detection of adulteration in saffron samples by diffuse reflectance infrared fourier transform spectroscopy

Accurate estimation of saffron purity is essential for ensuring product integrity, particularly in the presence of common chemical adulterants such as tartrazine, sunset yellow, and quinoline yellow. This study introduces the alternating conditional expectations (ACE) algorithm, coupled with diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS), as an innovative chemometric approach to address this challenge. The ACE algorithm utilizes optimal transformations for both independent and dependent variables, revealing non-linear relationships and maximizing linear effects between transformed variables. To prepare the spectral data for ACE modeling, we will perform moving average smoothing and Standard Normal Variate (SNV) transformation for preprocessing. Subsequently, we will employ the duplex algorithm to select both calibration and prediction sets from the preprocessed data matrix. The effectiveness of the ACE model was evaluated using several metrics, including the coefficient of determination (R2), adjusted R-squared (R2adj), sum of squared errors (SSE), Regression Sum of Squares (SSR) and mean squared error (MSE). ACE model exhibited excellent performance in determining saffron content even in the presence of adulterants like tartrazine, sunset yellow, and quinoline yellow, on both calibration and prediction sets. Our model achieved excellent performance on both datasets. On the calibration set, a high R2 value (0.9951) indicates a strong correlation between predicted and observed saffron content in the model. The R2adj (0.9950) further strengthens this conclusion by accounting for model complexity, suggesting the model avoids overfitting. These findings are complemented by robustness measures. The statistical parameters of the ACE model, such as total sum of squares (SST) (1.103∗104), SSR (1.098∗104), SSE (54.414), and MSE (0.878), were calculated to compare the performance of the developed methods for determining Saffron's concentration in each mixed sample.

Genetic and the Environmental Influences on the Concentrations Iron and Zinc in Small Seeded Common Bean (Phaseolus vulgaris L.) Varieties and Advanced Lines From Ethiopia

ABSTRACTCommon bean (Phaseolus vulgaris L.) is a grain legume rich in proteins and micronutrients, particularly iron and zinc. In this study, 30 small‐seeded genotypes were planted in five locations in Ethiopia, following an alpha lattice design with three replications, to determine environmental and genotypic influence on the Fe and Zn concentration. Based on their Fe and Zn contents, bean cultivars were evaluated for adaptability and stability using AMMI analysis. The Fe concentrations of raw bean seed varied from 44.4 to 84.4 μg/g within the panel of small‐seeded genotypes, with an average range of variance of 18 μg/g across environments, and its seed Zn concentrations varied from 19.7 to 32.3 μg/g, with an average range of variance of 12.6 μg/g across environments. The averages bean Fe concentration among the small‐seeded genotypes across sites in Ethiopia was 62.2 and 26.1 μg/g for Zn concentrations. Results from the analysis of variance using the AMMI model indicated that genotypes accounted for 20.53% and 9.49% of the total variance in seed Fe and Zn concentrations, respectively. The environment had a greater impact, affecting 60.92% and 81.52% of total sum of squares for Fe and Zn concentrations, respectively. According to the broad‐sense heritability, there appears to be some genetic control over Fe and Zn concentrations. However, the substantial effects of the environment and genotype‐by‐environment interaction on Fe and Zn concentrations in small‐seeded genotypes indicates breeding for higher amounts of trace minerals in new bean varieties could be a challenging task. This means the notion that beans can be biofortified to have higher concentrations of Fe and Zn might not be achievable in Ethiopia. A shift in breeding strategies that focuses on traits to enhance the bioavailability of Fe and Zn from bean is warranted and could be a solution to enhance the delivery of iron from small‐seeded beans produced in Ethiopia.

Analysis of the Phenotypic Adaptability and Stability of Wheat Genotypes through the Biplot Approach

The present study was conducted during the cropping season during October to April in the year 2022–23. Highly significant effects of environments, genotypes×environments interactions and genotypes had been observed as per the AMMI analysis of twelve wheat genotypes evaluated under advanced varietal trials at eleven major locations and Environments effects had augmented about 49% of the total sum of squares. ASV measure had utilized the 51.7% of the interaction effects had pointed for DBW296, HI1654, PBW899 while MASV measure had exploited nearly 98% of total interaction effects had selected the DBW296, WH1311, UP3111 genotypes as Superiority index measure also the suitability of same genotypes. The simultaneous selection index based on yield and ASV had pointed towards the HI1653, HI1654, HD3369 and genotypes HI1653, DBW397, WH1311 were identified by ssi MASV and genotypes HI1654, HD3369, HI1653 genotypes settled by ssi WAASB measure. Non parametric Si1 had pointed for DBW296, HD3369, NIAW3170 and the values of Si2 and Si3 had pointed towards DBW296, WH1311, HD3369. Composite non parametric measures First measure NPi(1) had favoured the DBW296, HI1653, HD3369 while as per NPi(2) values the DBW296, NIAW3170, PBW899 would be suitable genotypes. Out of the five clusters in the biplot analysis the ASV &amp; WAASB had clustered with NPi(1), Si2, Si3, Si4, Si5, Si6, Si7 non parametric measures as placed in the first quadrant.

Stability analysis to identify improved lines of cluster bean (Cyamopsis tetragonolobaL. Taub.)

AbstractTo select a stable and best-performing cluster bean line over seasons, an experiment was carried out using five shortlisted advanced breeding lines of vegetable cluster bean in randomized block design with four replications inKharifandSummerseasons of 2019–20 and 2020–21 at ICAR-Indian Institute of Horticultural Research, Bengaluru. Additive mean effect and multiplicative interaction analysis of variation indicated a significant genotype and environment (G × E) interaction for all the traits. A high environment effect of 33.73% of the total sum of squares was observed for the trait pods per cluster followed by yield per hectare, single pod weight, pods per plant and clusters per plant. The first two interactive principal component axes (IPCA) cumulatively contributed 87.9, 97.3, 94.6, 98.6 and 85.6% variations for yield per hectare, number of clusters per plant, pods per plant, pods per cluster and single pod weight, respectively, leaving a small but significant amount of variation in the third IPCA. A mean versus weighted average of absolute score (WAAS) biplot indicated that genotype IIHRCB 26-2-1 is stable and best for the trait clusters per plant (&gt;20 clusters/plant) while, IIHRCB 22-1-1 is superior and stable (WAAS mean nearly 0.00) for pods per cluster with &gt;5 pods/cluster, pods per plant with &gt;90 pods/plant, single pod weight with &gt;3.0 g and yield per hectare around 24 t/ha. For all the environments, genotype IIHRCB 22-1-1 was found ‘all-time winner’ for yield per hectare and single pod weight. Based on multitrait stability index, IIHRCB 22-1-1 was found the best performer and the most stable genotype.

PROTEIN CONTENT STABILITY OF TRITICALE GENOTYPES UNDER MULTIPLE ENVIRONMENTS USING AMMI ANALYSIS

Triticale is an important source of protein in animal nutrition, both as grain and silage. Protein is a quality criterion that is strongly affected by the environment and genetic factors. The objectives of this study were to assess genotype–environment (G-E) interactions and determine and compare stable genotypes to recommend for further evaluation. The protein content of 12 genotypes obtained from 7 environments were evaluated using additive main effect and multiplicative interaction (AMMI) analysis in the 2014–2015 and 2015–2016 growing seasons in 4 locations in Turkey. The variance of AMMI analysis based on protein content showed a significant effect of environment, genotype and G–E interaction, with a 93.0, 2.3 and 4.5% total variation, respectively. Partitioning of the total sum of squares showed that the effect of environment was a predominant source of variation, followed by the G–E interaction and genotype effect. AMMI analysis showed that the first principal component was highly significant at P &lt; 0.01. The mean grain protein content varied from 14.9 to 16.2% among the genotypes and from 10.9 to 18.8% among the environments. AMMI analysis indicated that G3, G12 and G1 were quite promising genotypes. G8, which was used as a variety candidate in this research, was quite stable but had a low protein value. In addition, G3, G11 and G12 had a higher protein content than the standard varieties evaluated in this research. G3 had both a high protein content and stable genotype; therefore, it can be recommended to release as a candidate. As AMMI analysis indicated, E1 and E7 environments were especially suitable for protein studies in triticale, while E4, E5 and E6 showed poor results and were not suitable environments.

Evaluation of yield stability in sunflower hybrids by AMMI analysis

A total of 15 sunflower hybrids and 3 checks with three season experimental data for seed yield have been utilized for stability analysis in sunflower. Study revealed that presence of significant difference among genotypes/seasons and also due to interaction of significance for PCA1, PCA2 and PCA3. Among these, PCA1 alone recorded 92.30 percent of total sum of squares. High yielding sunflower hybrids viz., CSFH 19004, CSFH 19087 and CSFH 19086 had PCA1 score close to zero indicating that these genotypes were less influenced by environments. Hence, the above said hybrids were stable and had general adaptability for both Kharif and Rabi seasons.

Influence of Genotype and Environment on Fruit Phenolic Composition of Olive

Extra virgin olive oil (EVOO) is renowned for its numerous minor compounds, particularly phenols, which contribute to its health-promoting properties and commercial quality. The phenolic composition of EVOO largely relies on the initial phenolic content of the olive fruits, which represents, therefore, an important trait to be considered in olive breeding programs. However, only limited studies have been conducted so far to compare the relative influence of genotype and environment in the variability of this trait. For that reason, this study aims to assess the influence of genotype (cultivars ‘Arbequina’, ‘Coratina’, ‘Hojiblanca’, ‘Koroneiki’, ‘Martina’, and ‘Picual’) and environment (harvest date, season, and location) on the fruit phenolic content and composition. A wide general variability was obtained in the whole dataset, with total phenols ranging from 6019 to 38,380 μg/g. A stronger effect of the genotype than the environment was observed for total phenolic content (representing 76–90% of total sums of squares) and the different groups of compounds. Notably, significant variations were found among cultivars not only in the total phenol content but also in the percentage of individual compounds within each main phenolic group. Overall, taking into account the entire dataset available, a clear grouping of samples according to genotypes was observed. The results obtained highlight the prominent influence of the genotype (cultivar) over the environment and genotype–environment interactions on the phenolic content and composition in olive fruits; even when considering very contrasting environments as the Mediterranean and subtropical in the present work. These findings suggest the feasibility of breeding selection of new cultivars with distinctive phenolic content and composition.

Performance assessment of wheat genotypes based on the superiority index using additive main and multiplicative interaction effects and BLUP analysis

The simultaneous use of additive main and multiplicative interaction effects (AMMI) and best linear unbiased predictors (BLUP) has been reflected in the multi-location evaluation of trials for number of crops. The additional advantages of both these approaches would be combined in superiority index (SI) to have an edge over the commonly used approaches. The promising wheat genotypes had been considered under multi location trails in Peninsular zone of India during the cropping seasons of 2018-2019 and 2019-2020. The highly significant environmental effects contributed 44.1% &amp; 35.3% of total sum of squares in the AMMI analysis, 20.6% &amp; 26.2% were augmented by G × E interaction, while 10.8% &amp; 7.5% were contributed by the genotypes.Wheat genotypes of UAS3001, MACS6222, GW322, and DDW48 expressed their superiority in BLUP values. Superiority indexes and adaptability measures had identified WHD964 and DDW48 genotypes for the second year of study. More than 75% variations among the considered measures were due to the first two interaction principal components (IPCA’s) under Biplot analysis. Number of superiority index measures were clustered with adaptability measures in the same quadrant. Superiority index, the weighted measure of yield and consistent performance of genotypes would be more appropriate for stability and adaptabilities studies.

Relationships among apparent electrical conductivity and plant and terrain data in an agroforestry system in the Ozark Highlands

AbstractMinimal research has been conducted relating apparent electrical conductivity (ECa) surveys to plant and terrain properties in agroforestry systems. Objectives were to identify: (i) ECa–forage yield, –tree growth, and –terrain attribute relationships within ECa‐derived soil management zones (SMZs) and (ii) terrain attributes that drive ECa variability within a 20‐year‐old, 4.25‐ha, agroforestry system in the Ozark Highlands of northwest Arkansas. The average of 12 monthly perpendicular (PRP) and horizontal coplanar (HCP) ECa surveys (August 2020 to July 2021) and 14 terrain attributes were obtained. Tree diameter at breast height (DBH) and height (TH) measurements were made in December 2020 and March 2021, respectively, and forage yield samples were collected during Summer 2018 and 2019. Apparent EC‐tree property relationships were generally stronger within the whole site (averaged across tree property and ECa configuration, |r| = 0.38) than within the SMZs (averaged across tree property, ECa configuration, and SMZ, |r| = 0.27). The strength of the SMZs’ terrain‐attribute‐PRP‐ECa relationships were 9% to 205% greater than that for the whole site. In whole‐site, multi‐linear regressions, slope length and steepness factor (10.5%), mid‐slope (9.4%), and valley depth (7.2%) had the greatest influence (i.e., percent of total sum of squares) on PRP ECa variability, whereas valley depth (15.3%), wetness index (11.9%), and mid‐slope (11.2%) had the greatest influence on HCP ECa variability. Results show how ECa relates to plant (i.e., DBH, TH, and forage yield) and terrain data within SMZs in agroforestry systems with varying topography and could be used to precisely manage agroforestry systems.

Durum Wheat Field Performance and Stability in the Irrigated, Dry and Heat-Prone Environments of Sudan

Developing climate-resilient crop varieties with better performance under variable environments is essential to ensure food security in a changing climate. This process is significantly influenced, among other factors, by genotype × environment (G × E) interactions. With the objective of identifying high-yielding and stable genotypes, 20 elite durum wheat lines were evaluated in 24 environments (location–season combination) during 5 crop seasons (2010/11–2014/15). The REML (residual maximum likelihood)-predicted means of grain yield of 16 genotypes that were common across all environments ranged from 3522 kg/ha in G201 to 4132 kg/ha in G217. Results of additive main effect and multiplicative interaction (AMMI) analysis showed that genotypes (G), environments (E), and genotype × environment interaction (GEI) significantly affected grain yield. From the total sum of squares due to treatments (G + E + GEI), E attributed the highest proportion of the variation (90.0%), followed by GEI (8.7%) and G (1.3%). Based on the first four AMMI selections for grain yield in the 24 environments, genotypes G217, G219, G211, and G213 were selected in 23, 12, 11, and 9 environments, respectively. The genotype and genotype × environment biplot (GGE) biplot polygon view showed that the environments were separated into three mega-environments. The winning genotypes in these mega-environments were G217, G214, and G204. Genotypes G212, G220, G217, G215, and G213 showed low AMMI stability values (ASV), whereas genotypes G217, G220, G212, G211, and G219 showed low genotype selection index (GSI), indicating their better stability and adaptability to the test environments. The results indicated that genotypes G217, G219, G211, G213, and G220 combined both high grain yield and stability/adaptability under dry but irrigated and heat-prone environments. An in-depth analysis of the superior genotypes could help better understand the stress-adaptive traits that could be targeted to further increase durum wheat yield and stability under the changing climate.

Effects of Combined Long-Term Straw Return and Nitrogen Fertilization on Wheat Productivity and Soil Properties in the Wheat-Maize-Soybean Rotation System in the Pannonian Plain

The study, conducted to evaluate the effects of long-term straw management combined with the application of increasing nitrogen rates on the yield of twenty winter wheat varieties, as well as on soil properties, was carried out in a long-term field trial established in 1971. The trial was monitored for twenty growing seasons under rainfed conditions in a typical chernozem zone of the southern part of the Pannonian Plain. The cropping system was a winter wheat-maize-soybean rotation. The ten SN-treatments (combinations of straw management (S) and N-fertilization) were as follows: In the plot (treatment) with straw return (S1), seven variants of nitrogen fertilization (0–180 kg N ha−1) were included, while on the plot without straw return (S0) the variants of N-fertilization were 0, 90 and 150 kg N ha−l. Based on the high relative share in the total sum of squares, variance analysis showed that wheat grain yield (GY) was significantly affected by years, SN-treatments, and their interaction, and they can explain the largest part of the total variance of GY. The results showed that straw return integrated with N fertilization could increase wheat yield to varying degrees over 20 years. On average, for all years, the highest GYs were obtained in the treatment S1 and fertilization with 180 and 150 kg N ha−1. The overall results showed that long-term straw returning significantly increased GY by an average of 8.4 ± 4.5%, with a considerable simultaneous increase in yield stability compared to straw removal. In addition, straw incorporation (SI) significantly increased soil humus, total nitrogen (TN), and soil organic carbon (SOC) contents at a soil depth of 0–30 cm by an average of 4.2, 3.8, and 11.3%, respectively. The results of our study have demonstrated that the long-term practice of straw return, in combination with the application of mineral fertilizers, has the potential to serve as a sustainable soil management strategy that is economically viable and environmentally acceptable. However, additional research is required to investigate its interactive effects on both grain yield and soil productivity.

Genetic Variability and Heritability of Seed Protein Content in Vigna unguiculata L. Walp. Genotypes Grown in Two Agro-Ecological Environments of Chad

Cowpea is an important pulse crop grown in sub-Saharan Africa and in parts of Asia and the Americas. Cowpea seed is an affordable source of nutrients rich in protein with number of essential amino acids. Dehulled seeds of eight improved cowpea lines grown in N’Djamena and Bebedjia (Chad) were analyzed for seed protein content in order to assess the variability, the heritability and the effect of environment interaction. In each locality, the experimental design was a triplicated randomly complete block design. The results showed a wide variability among genotypes for crude seed protein content. The mean value of crude seed protein content was 25.55% with varieties TN-27-80, TN-985-61399 and TN-5-78 showing highest values. High heritability in broad-sense (h2 = 0.74) and moderate genetic advance (GA = 14%) estimated for this character indicated the scope for improvement through selection. The effects of genotype (83.1% of the total sum of square), location (3.5%) and their interaction (13.4%) were highly significant (P&lt;0.01), but the protein content was slightly influenced by the environment. High potential for breeding programs is expected as genetic factors are believed to account for the main variation in protein content. This research could provide information for breeders to develop cowpea cultivars with higher protein content.

Evaluation of Indian ginseng [Withania somnifera (L.) Dunal] breeding lines and genotype-by-environment interaction across production environments in western India.

In order to find location-specific and broadly adapted genotypes for total root alkaloid content and dry root yield along with additive main effects and multiplicative interactions (AMMI) and genotype (G) main effects plus genotype × environment (E) interaction in Indian ginseng (Withania somnifera (L.) Dunal), (GGE) biplot analyses were used in the current study. Trials were carried out in a randomized complete block design (RCBD) over three succeeding years viz., 2016-2017, 2017-2018 and 2018-2019 at three different locations (S. K. Nagar, Bhiloda and Jagudan). Analysis of variance (ANOVA) for AMMI for dry root yield revealed that the environment, genotype, and GE interaction, respectively, accounted for significant sums of squares of 35.31%, 24.89%, and 32.96%. For total root alkaloid content, a significance of 27.59% of total sum of squares was justified by environment, 17.72% by genotype and 43.13% by GEI. Nine experimental trials in total were taken into consideration as contexts for the GEI analysis in 16 genotypes, including one check. AMMI analysis showed that genotypes, SKA-11, SKA-27, SKA-23 and SKA-10 were superior for mean dry root yield and SKA-11, SKA-27 and SKA-21 had better performance for total root alkaloid content across environment. The GGE biplot analysis showed genotypes SKA-11, SKA-27, SKA-10 desirable for dry root yield and SKA-26, SKA-27, SKA-11 for total root alkaloid content. As a result of the GGE and AMMI biplot techniques, SKA-11 and SKA-27 were determined to be the most desired genotypes for both total root alkaloid content and dry root yield. Further, simultaneous stability index or SSI statistics identified SKA-6, SKA-10, SKA-27, SKA-11 and AWS-1 for higher dry root yield, whilst SKA-25, SKA-6, SKA-11, SKA-12 and AWS-1 for total alkaloid content from root. Based on trait variation, GGE biplot analysis identified two mega-environments for dry root yield and a total of four for total root alkaloid content. Additionally, two representative and discriminating environments-one for dry root production and the other for total root alkaloid content were found. Location-specific and breeding for broad adaptation could be advocated for improvement and release of varieties for Indian ginseng.

Predicting firm performance and size using machine learning with a Bayesian perspective

This paper investigates the issue of predicting the financial performance of firms in registered manufacturing in developing countries using machine learning methods along with economic theory to explain the findings. While literature suggests that predictability of top line measures related to sales is lower compared to bottom line measures such as net profits for small informal establishments in developing countries, we find the opposite holds true for firms in registered manufacturing in the food processing industry in India based on the results from machine learning techniques of Bayesian additive regression trees (BART), boosted trees, bootstrap forests, and regression tree algorithms. BART models in validation outperformed the other algorithms in predictability of the dependent variables. Across ten validation studies, BART had an average R2 ranging from 0.922 to 0.934 and boosted tree models had an average R2 ranging from 0.873 to 0.905 for predicting sales. A key significant independent variable for predicting sales across all categories and algorithms was real raw material expenses explaining approximately 83% to 88% of the total sums of squares in all validations. This is in line with the realities of the food processing industry, which is intensive in its raw material usage. The dependent variable ‘profits’ (as measured by real profits before depreciation, interest, taxes and amortization, ‘real_pbdita’) was more difficult to predict relative to sales. BART models again outperformed the other algorithms in validation with an average R2 ranging from 0.745 to 0.818. Key significant variables in the models were more diverse where raw material expenses, compensation to employees, and net- or total-fixed assets explained the largest proportions of the total sums of squares. The results from a machine learning approach with a Bayesian perspective can enhance the understanding of the mechanisms that translate sales into profits for registered manufacturing, thereby aiding policy-making for small businesses in the formal sector in developing countries.

Stability analysis of short duration rice genotypes in Telangana using AMMI and GGE Bi-plot models

In order to identify stable short duration rice genotypes across different agro-climatic zones in Telangana state, Additive Main Effects and Multiplicative Interaction Models (AMMI) and GGE Bi-plot analyses was performed. Analysis of variance clearly revealed that genotypes contributed highest (34.57 %) followed by environments (32.31 %) and genotype environment interaction (17.10 %) in total sum of squares indicating very greater role played by genotypes, environments and their interactions in realizing final grain yield. AMMI analysis revealed that rice genotypes viz., KNM 2305 (G12), KNM 2307 (G16) and JGL 20776 (G9) were recorded higher mean grain yield with positive interactive principal component analysis 1 (IPCA1) scores whereas, KNM 2307 (G16) and RNR 23595 (G5) were plotted near to zero IPCA1 axis indicating relatively more stable performance across locations. However, the GGE Bi-plot genotype view depicts that the genotypes viz., RDR 1188 (G6) and KNM 2305 (G12) were known as highly unsteady across locations. Among environments, Rudrur (E4), Kunaram (E2) and Rajendranagar (E5) locations were identified as relatively ideal to realize good yields whereas Jagtial (E1), Kampasagar (E3) and Warangal (E6) locations were poor and most discriminating. Among the six locations, the performance of genotypes was relatively similar in Kunaram (E2), Kampasagar (E3) and Rudrur (E4), Warangal (E6) though they belong to different agro-climatic zones of Telangana state, whereas Jagtial (E1) location seems to be little divergent. Further, KNM 2305 (G12) and US 314 (G17) were performed better at Jagtial (E1) and Rajendranagar (E5) while MTU 1010 (G8) was found to have good performance in Rudrur (E4) and Warangal (E6) locations. The results conclude that KNM 2305 was high yielder but found to be unstable across locations whereas KNM 2307 (G16) and KPS 6251 (G15) were identified as good with reasonably higher grain yield and stable performance over locations.

Genotype × environment interaction of lowland bread wheat varieties for irrigation in different areas of Oromia.

Ethiopia is the leading wheat producer in Sub-Saharan Africa, and the productivity has increased in the last few years. There is also a potential for irrigated wheat production in the lowlands, even though its cultivation is at infant stage. The experiment was conducted in the Oromia region at nine locations in 2021 with irrigation. The study aimed to select high yielding and stable bread wheat variety/ies for lowland areas. Twelve released bread wheat varieties were tested using randomized complete block design with two replications. Environment had the largest effect, 76.5% of total variability, while genotypes 5.0% and GE interaction 18.5% explained total sum of squares. The average grain yield of varieties across locations ranged from the lowest 1.40 t ha-1 at Girja to the highest 6.55 t ha-1 at Daro Labu, with a grand mean of 3.14 t ha-1. The result showed that varieties released for irrigated areas, Fentale 1, Ardi, and Fentale 2, were ranked the top three based on overall environment mean grain yield. The first and second principal component account 45.5% and 24.7% of the genotype by environment interaction (G × E), respectively, explained 70.2% of the total variation. Daro Lebu and Bedeno were the most productive environment, while Girja was the least productive of irrigated bread wheat for lowlands of the Oromia region. Genotype Selection Index (GSI) showed that varieties Fentale 2, Fentale 1, Pavon 76, and ETBW9578 are stable and high yielding. Based on AMMI and GGE biplot analysis, Girja indicated the most discriminating area and Sewena as representative environment for selecting wide adaptable irrigated lowland varieties. The results of the present study indicated that Fentale 2 and Fentale 1 showed better yield stability across all test environments, therefore, these bread wheat varieties are recommended for wide cultivation in irrigated areas of the Oromia region.

Genotype-environment Interaction for Yield and Yield Associated Traits in Chilli

The present study was conducted on six chilli genotypes across 4 environments in randomized complete block design with three replications during 2019-20 to evaluate the performance of chilli genotypes with higher yield and stability. The AMMI (Additive main effect and Multiplicative Interaction) model was used to estimate the genotype-environment interaction. Genotypes and environments were significantly varied for all the traits, which revealed the presence of genetic variability in the materials under study. The total sum of squares for genotype (G), environment (E) and G × E interactions ranged from 27.44 to 80.49%, 1.26 to 45.09% and 14.60 to 62.67% interactions respectively. The genotypes account large proportion (&gt; 50%) of total variation for Number of fruits per plant and fruit length, which means that genotype, was more important factor for these traits. GGE biplot methodology was used for graphical display of yield data after subjecting the genotypic means of each environment to GGE biplot. Principle components Analysis (PC1 and PC2) were used to depict the stability and superiority performance of the genotypes for yield and yield related traits. the chilli genotypes Co631(G6) and Co632 (G5) were stable and had comparatively less fluctuation for yield and yield related trait across environments which was found promising for recommendation and release for wider adapted variety. Cross over GEI across environment and among genotypes showed that the most general adopted environment with high mean performance, Magura has the best condition for growing chilli.

Identification of stable lentil genotypes through genotype by environment interactions on yield potential in Morocco

Genotype by environment (G×E) interaction study becomes essential for selecting high and stable yielding genotypes. Altogether 64 lentil genotypes representing improved varieties, landraces and advanced lines were evaluated under 6 environments for green cover, phenological characters, grain yield and 1000 seed weight. Variance analysis revealed highly significant effects of genotype, environment and genotype by environment interaction for all studied traits. The environment had the greatest effect with 75.7% of the total sum of squares. AMMI-GGE biplot identified 3 mega-environments where Z32 advanced lines were performed in the first one (E1, E4 and E5); Z33 was the best in the second mega-environment (E2 and E3), of which E2 (SAD18) was characterized as discriminating and representative environment for selecting adaptable genotypes. While VR4 and LR4 were the winning genotypes in the third mega-environment represented by E6. According to 7 stability methods, Z33, Z32, Z31, Z13 and G03 lines were the most stable and resilient in all environments. In addition, five landraces (PA6, LR4, LR10, LR6 and PA1) showed a high yielding potential that could be used as a source of genotype candidates to develop novel resilient varieties of lentils. Varieties VR9 was recommended for both favorable and unfavorable environments, VR6 for unfavorable and VR3 for favorable environment. Otherwise, genotypes were grouped into 3 clusters with 90% of similarity. The third one gathered the highest yielding genotypes (Z33 and Z32), which were the most stable that could be promoted for developing resilient varieties for climatic changing environments.