Stability Assessment for Improved Mustard Production in Ecologically Diverse Regions of Jharkhand: Insights from AMMI and GGE

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Significant differences among the genotypes are observed, with environmental factors and their interactions exerting a considerable influence. Utilizing the AMMI and GGE biplot methods, the study delves into the intricate interactions affecting economically vital traits such as seed yield per plant and oil percentage. The combined effect of environment and interaction explains a substantial portion of the observed variation of 79.6 and 58.9% on seed yield per plant and oil % respectively. The first two principal components together explained larger portion of 85.2% and 89.5% of the GXE variation of seed yield per plant and oil % respectively. The AMMI analysis had identified that, the genotypes Kranthi, PA-5232 and BAUM-09-12-1 for seed yield per plant and BAUM-08-18, Shivani, DRMRCI-70 and Pusa Bold for oil % are stable performers. The GGE biplot analysis and AMMI have commonly identified BAUM-09-12-1 and Pusa Bold as high yielding and most stable for seed yield per plant and oil % respectively. The results of AMMI identified ranchi as most ideal environment for selection of genotypes for both seed yield per plant and oil%, but GGE differs in-terms with Ranchi as ideal only for oil% and dumka for seed yield per plant. The availability of the above information of genetic variability and stability of genotypes for seed yield per plant and oil % can aid improving mustard production levels and self-sufficiency in edible oils. Genetic variability AMMI GGE and stability Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Feeding the rapidly growing global population amidst escalating climate adversities is a paramount task facing the agricultural community. Besides food grains, oil seeds hold greater importance by ranking fourth among all the crops. Among the several oil seed crops, mustard is an important edible oil seed crop belonging to the Brassicaceae family with a diploid genome 2n = 36. It is the world’s most important crop in-terms of edible oil after soya bean and oil palm. Mustard possesses huge potential in-terms of health benefits. The consumption of mustard oil can overcome obesity (Chhajed et al., 2021 ). The antioxidant rich composition of mustard seed can provide the protection for biochemical from oxidative damage (Dua et al., 2014 ). Moreover, diets based on mustard oil have been shown to elicit hypoglycaemic effects through enhanced insulin activity (Sukanya et al., 2020 ). The extensive cultivation of rapeseed and mustard in India underscores their significance, covering an area of 7.9 million hectares with a productivity of 1.49 tons per hectare, resulting in a total production of 11.9 million tons. However, the growing needs and demands for oilseeds, including mustard, remain unmet. But needs and demand of oil seeds along with mustard have not been met. The further increase in edible oil imports to 16.47 million tons during the year 2022–2023 emphasizes the greater need to bolster oil seed production in our country. This enhanced the immediate need for enhancing the production of oil seeds crop including mustard. The central objective of plant breeding to improve the yield levels is significantly challenged by the polygenic nature of the trait. The production levels of mustard are witnessing huge fluctuations due to lack of adaptability to the wide range of agroclimatic conditions existing in various agro-climatic regions. The differential response of the crop cultivars in differential environments was the result of genotype x environment interaction (Kang, 1998 ). The analysis of variance can provide a preliminary understanding on existence of impact of the environment and its interactions. In order to develop varieties with stable yields irrespective of environment, there is a need to delineate the environmental impacts on the genotypic performance which is possible by the application of stability analysis models. Several statistical concepts like eco-valence-based stability (Wricke, 1965 ), analysis models like Eberhart and Russel (Eberhart & Russell, 1966 ), Freeman and Perkins (Freeman & Perkins, 1970 ), Finlay and Wilkinson model (Finlay & Wilkinson, 1963 ), AMMI and GGE are used in mustard (Abu et al., 2022 ; Chauhan et al., 2013 ; Tadesse et al., 2018 ) and several other oils seed crops (Kebede, Worku, Jifar, et al., 2023; Khan et al., 2021 ; Memon et al., 2023 ; Zhou et al., 2021 ) for identifying the stable genotypes. Among these, the methods AMMI and GGE are being used to identify stable genotypes across environments which can aid in crop improvement of mustard. The current study for identification of existing diversity and associations among the genotypes and most stable genotypes across locations can improve mustard production levels. Materials and Methods Plant Materials In the current study, 15 genotypes of mustard (Table 1 ) including hybrids, open-pollinated varieties, and locally and nationally released varieties are collected from various sources. Table 1 15 genotypes of Brassica Juncea evaluated at four locations of Jharkhand. S. No Name of Genotype Source 1. SHIVANI Department of GPB, BAU, Ranchi 2. 45S35 PHI Seeds 3. KESARI GOLD BAYER Bio-Science 4. BAUM-09-12-1 Department of GPB, BAU, Ranchi 5. DRMR1153-12 DRMR, Bharatpur 6. RH-0919 CCHAU, Haryana 7. DRMRCI-70 DRMR, Bharatpur 8. NRCHB-101 DRMR, Bharatpur 9. 45S46 PHI Seeds 10. PUSA BOLD IARI Pusa New Delhi 11. PA-5232 BAYER Bio-Science 12. KRANTHI CSA, Kanpur 13. 45S47 PHI Seeds 14. PA-5210 BAYER Bio-Science 15. BAUM-08-18 Department of GPB, BAU, Ranchi Experimental Location and Design The genotypes were planted using a randomized block design with three replications across four distinct agro-climatic regions of Jharkhand named, Ranchi Agricultural College farm in Ranchi (latitude 23.23°N, longitude 85.23°E), ZRS Chainki (latitude 22.12°N, longitude 83.20°13´ E), ZRS Dumka (latitude 22°33´N, longitude 86°30´E), and ZRS Darisai (latitude 24°16´N, longitude 87°15´E) during the rabi season of 2020-21. Each genotype was allocated to 10-row plots, each measuring 5 meters in length. The spacing between rows and plants was maintained at 30cm and 10cm, respectively, and standard agronomic practices were followed. Data Collection The trait yield per plant (g) was recorded for five randomly selected plants, and the average values were considered for analysis. The oil percentage (%) was estimated using near-infrared spectroscopy (NIRS) with the Pfeuffer-GmbH GRANOLYSER. Seed samples used for oil percentage (%) measurement were poured into a beaker up to a quantity of 600 ml, and the oil content for each sample within a replication was measured twice, with average values recorded. The wavelength range utilized was 950–1540 nm, and the process oif measurement took approximately 30 seconds. Statical analysis The data analysis was conducted utilizing R software (version 4.3.2). The Bartlett test was applied using GenStat version 22.0 to assess homogeneity of variances. Pooled analysis of variance (ANOVA) was executed employing the agricolae package (version 1.3-7) to evaluate the significance of genotype and environmental effects. The heat maps of genotype indicating performances across environments were generated with the tidyverse (version 2.0.0) and ggplot2 (version 3.4.4) packages. Furthermore, AMMI analysis was performed utilizing the metan (version 1.18.0) package, combined with ggplot2 (version 3.3.4) for graphical visualization, to elucidate genotype by environment interaction effects. Results and Discussion The Bartlet test's non-significance (p > 0.05) has proven the presence of error variance homogeneity among the four test environments and pooled analysis was carried out further. The ANOVA for seed yield plant − 1 and oil percentage (%) is presented in Table 3 and Table 4 . The profound impact of genotypes, environments, and genotype x environment on seed yield plant − 1 and several other related traits was evident from a pooled analysis of ANOVA which was in agreement with several existing studies (Chauhan et al., 2013 ; Kumar et al., 2018 ; Sagolsem et al., 2013a ; Sah et al., 2015 ). The lack of stable performance in the genotypes enhanced the need for stability studies to decode the genotype x environmental interactions and identify the most stable performing genotypes among the test locations. Additive main effects and Multiplicative interaction model (AMMI): The AMMI analysis for grain yield and oil % has revealed that the significant impact of environment, genotypes and environment x genotype components (Tables 2 and 3 ). The significant impact of environment and genotype X environment interaction was reported in mustard (Sagolsem et al., 2013b ) and several other oil seed crops(Amala Balu et al., 2007 ; Kona et al., 2024 ). In case of seed yield per plant, the components of environment, genotypes and GXE account for 64.1 %, 10.8 % and 15.5 % respectively. Like seed yield per plant, the ifluenceof componets followed the same order in the case of oil percentage i.e., environment, genotypes and environment x genotype components account for 37.4%, 11.37% and 21.55% respectively. Compared to genotypes, the greater impact of environment and environment x genotype interaction on traits of importance i.e., seed yield per plant and Oil percentage (%) enhanced the need for stability studies. Table 2 Ammi analysis Table for Seed Yield per Plant Source DF Sum Sq Mean Sq F Value Pr (> F) Proportion Accumulated ENV 3 492.4 164.123 69.08 0.00000463 64.4 NA REP(ENV) 8 19 2.376 5.2 0.0000155 2.48 NA GEN 14 82.8 5.911 12.94 1.46E-17 0.8 NA GEN: ENV 42 118.4 2.819 6.17 7.07E-15 15.5 NA PC1 16 66.7 4.17 9.13 0 56.3 56.3 PC2 14 34.1 2.438 5.34 0 28.8 85.2 PC3 12 17.6 1.464 3.2 0.0006 14.8 100 Residuals 112 51.2 0.457 NA NA 6.7 NA Total 179 763.8 - - - - - Table 3 Ammi analysis Table for Oil percentage (%) Source DF Sum Sq Mean Sq F Value Pr (> F) Proportion Accumulated ENV 3 127.03 42.342 26.73 1.60E-04 37.4 NA REP(ENV) 8 12.67 1.584 2.02 4.98E-02 3.7 NA GEN 14 38.6 2.757 3.52 8.67E-05 11.37 NA GEN: ENV 42 73.16 1.742 2.23 4.58E-04 21.55 NA PC1 16 50.24 3.14 4.01 0.00E + 00 68.7 68.7 PC2 14 15.22 1.087 1.39 1.70E-01 20.8 89.5 PC3 12 7.71 0.642 0.82 6.29E-01 10.5 100 Residuals 112 87.65 0.783 NA NA 21.2 NA Total 179 338.96 1.8654967 AMMI Biplot 1: The Ammi-biplot 1 is the plot resulted from the main effects of grain yield and IPCA 1 scores of genotypes and environments against each other. The first two principal components together have explained 85.2% and 89.5% of the variation in seed yield per plant and oil % respectively, which account greater percent of genotype X environment interactions. The outcome of greater variation explained by first two PCs was in agreement with existing study (Kona et al., 2024 ). The ordinate divides the biplot into four sections of which the sections towards right side indicate higher trait performance compared to mean. From Figs. 1 and 3 , the dispersion of genotypes across four quadrants indicates differential response of genotypes in-terms of seed yield per pant and oil % across study locations. The genotypes 45S47, Kesari gold, BAUM-09-12-1, 45S46, PA-5210, 45S35, DRMR-1153-12, BAUM-08-18 fallen under quadrants located right side indicates superior performance for seed yield per plant (Fig. 1). Similarly, genotypes DRMR-1153-12, DRMR-CI-70, NRCHB-101, BAUM-08-18, PUSA BOLD, BAUM-09-12-1, Shivani, Kesari Gold and 45S46 observed under right side quadrants are superior in-terms of oil % (Fig. 3 ). Among the test environments, Chiyanki in case of seed yield per plant and the environments Darisai and dumka in-case of oil % has fallen under the quadrants with higher grain yield than the mean. AMMI Biplot 2: The biplot 2 resulted from plotting IPCA 1 and IPCA 2 scores against each other which can divulge the genotype and environment interactions. In case of both seed yield per plant and oil % the environment 1 vector was shorter when compared to others (Fig. 2 and Fig. 4). The environment vector which much proximity to zero axis i.e., environment 1 for both seed yield per plant and oil % would be ideal for the selection of the most stable genotypes. The genotypes Kranthi, PA-5232 and BAUM-09-12-1 for seed yield per plant and BAUM-08-18, Shivani, DRMRCI-70 and Pusa Bold for oil % are more proximate to origin (Fig. 2 and Fig. 4). The proximity of above genotypes in respective traits represent the stable performance across the environments when compared to genotypes that dispersed across biplot way from origin. The genotypes 45S47 and Kesari gold for Env 2, Shivani and RH-0919 for Env 4 and PA-5210 for Env 1 and NRCHB-101 for 3 and BAUM-0818 for env1 are specifically adapted in terms seed yield per plant performance. Similarly, the genotypes Kranthi for env 2, 45S46 and 45S47 for env 3, BAUM-09-12-1 for env 4 and DRMR-1153-12 for env 1 most adaptable genotypes for oil % GGE Biplot (Which-won-where): The principal components PC1 and PC2 together accounted 78.5% of variation which explains the combined impact of genotype and GXE interactions. From the figure, the polygon has been divided into 7 components with 2,1,3,2,2,2 and 3 genotypes in each component respectively. The genotypes BAUM-08-18 in Kanke, Shivani in Chiyanki, G3 in Dumka and DRMR-1153-12 in Darisai are highly stable and superior in terms of yield. The genotypes lying at the vertex of the polygon are superior and best in the particular environment falling in the respective sectors (Yan & Tinker, 2006 ). The genotypes 45S35, 45S46, BAUM-09-12-1, PA-5210, NRCHB-101, RH-0919, KRANTHI and DRMRCI-70 are less responsive to the environment. The impact of the environment is less profound on the genotypes which are positioned inside the polygon (Khan et al., 2021 ). The genotypes SHIVANI, PA-5232 and Pusa Bold are poor performers across all the environments. The genotypes positioned at the vertex of the polygon positioned in sector which is unoccupied by any of the environment indicates its low performance for a particular trait (Ruswandi et al., 2021 ). The environments falling in the same sector of the polygon are represented as the mega environment (Choudhary et al., 2020 ). As the four environments are dispersed across different sectors, no mega environment has been identified. Similarly in case of oil%, the genotypes DRMR-1153-12 and BAUM-08-18 are the vertex genotypes in both the environments Ranchi and Darisai indicating their superiority in the respective environments. Mean vs Stability: The mean vs stability bi-plot (fig) helps us to evaluate the genotypes based on the mean performance and stability. The two lines i.e., abscissa (horizontal) and ordinate (vertical) constitute the biplot graph and the arrow mark on the abscissa indicates the order of the well performing genotypes for the trait evaluated. The ordinate groups the genotypes i.e., which falling on the right and left of it are classified as high and low yielding respectively. The projection length of genotypes from abscissa is the measure of their stability. Based on the above two considerations and from the fig, the genotypes for seed yield per plant and oil percentage (%) are classified into four groups i.e., high yield and highly stable, high yielding but less stable, less yielding but highly stable and less yielding less stable are tabulated (table). Among the several genotypes under study, PA-5210 is most stable and high yielding in-terms of seed yield per plant. In case of oil percentage (%), BAUM-08-18 is the most stable and high performing genotype. Table 4 Classification of genotypes based on Mean vs Stability Plot Genotypes Seed Yield per plant Oil percentage (%) High yielding Low yielding High yielding Low yielding Highly stable 45S35,45S46 and BAUM-09-12-1 PA532, Kranthi and DRMRCI-70 Pusa bold, BAUM-08-19, NRCHB-101 and DRMRCI-10 PA-5232, PA-5210 Less stable PA-532, Kranthi and RH-0919 Shivani, Pusa bold DRMR-1153-12 45S46, 45S47, Kranthi Ranking of Environments: The discriminative power and representativeness of environment was basis of the ranking of the environments. From the fig, in case of seed yield per plant, the environment dumka is closer to inner circle followed by darisai, ranchi and chiyanki. Similarly in case of oil %, the environment ranchi was closer to inner circle followed by darisai, dumka and Chiyanki. Based on the biplot ranking of environments, the environments closer to inner concentric circle are most desirable in terms of ability to discriminate the genotypes and to be representative of all the environments considered(Kebede, Worku, Feyissa, et al., 2023). The environments dumka and ranchi ae most ideal environments for testing and evaluating the genotypes for seed yield per plant and Oil % respectively. Relationship among Environments: The environmental vectors represented in biplot (Fig) provides information about relationship among the environments studied. In case of seed yield per plant, the cosine angle among the environments Ranchi and Darisai, Ranchi and Dumka, Darisai and Dumka, Chiyanki and Dumka was less than 90 (acute) and greater than 90 (obtuse) in case of Ranchi and Chiyanki and Chiyanki and Darisai. The length of Ranchi vector was smaller when compared to others. The cosine angle among the environments Ranchi and Chiyanki, Ranchi and Darisai and Ranchi and Dumka, Chiyanki and Darisai was less than 90 and the angle between the environments Dumka and Chiyanki was obtuse. The acute angle between the environmental vectors indicates the positive correlation and the obtuse angle is indicative of the inverse relationship among them (Yan & Tinker, 2006 ). The environments Ranchi and Darisai, Ranchi and Dumka, Darisai and Dumka, Chiyanki and Dumka in case of seed yield per plant and environments Ranchi and Chiyanki, Ranchi and Darisai and Ranchi and Dumka, Chiyanki and Darisai for oil % are positively correlated. The environments Chiyanki and Ranchi, Chiyanki and Darisai in seed yield per plant and Dumka and Chiyanki are negative correlated. The perpendicular alignment of environmental vectors signifies their independence, while varying lengths suggest degrees of correlation, with similar lengths indicating stronger associations (Haruna et al., 2017 ). The strong association between the environments is very useful in saving huge resources by avoiding the need of evaluating genotypes at multiple environments. Conclusion The prominence of environmental influence on performance of mustard genotypes was evident. The AMMI and GGE biplot has provided the key insights on the performance and stability of the stability of the genotypes. The substantial portion of the variation among the performance of the genotype was due to the environment and its interactions. The AMMI and GGE has identified the most and performing genotypes for across and the specific locations. The information on the environments that can distinguish superior performing mustard genotypes for seed yield per plant and oil % are identified. The genotypes BAUM-09-12-1 and Pusa Bold are high yielding and most stable for seed yield per plant and oil % respectively. The environment Ranchi has been commonly identified as core site for testing for Oil % by AMMI and GGE and Dumka has been identified test environment to differentiate the genotypes for seed yield per plant GGE. This information can contribute to the promotion of mustard cultivation across the Jharkhand. Declarations Competing Interests: The authors declare there are no competing interests. Author Contribution Niraj Kumar, Chandrasekhar Mahto, Hemchandra Lal, Binay Kumar and Himanshu Dubey has conceived and planned the experiments. Akhil kumar has carried out the experiment and took lead in manuscript preparation. Ekhalaque Ahmed, Sunil Kumar, Pradeep Prasad are involved the carrying out experiment. Kommineni Jagadeesh has involved in data analysis and manuscript preparation. Arun kumar has provided critical inputs and helped in shaping manuscript. The results were discussed among all the authors and were in agreement with each other. 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Kumar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYDACHh6GAw8bGBj42ZsPALkSMsRpSQRqkew5lgDSwkOUFgaQFoMbOQZgPkEd/D1nDx5I3GGX2HAj5/OrGzUWPAzsh49uwKdF4mxfwoHEM8mJjT1vt1nnHAM6jCct7QZea87zGBxIbGNObGbP3WacwwbUIsFjhleLPERLfWIbQ84z45x/RGgxONsD0nI4sYcjh/lxbhsRWgzPnAFqOXPceAbPMTPm3D4JHjZCfpE7k2P84eOOatn9x5sff875VifHz374GH7vIwE2CTBJrHIQYP5AiupRMApGwSgYOQAAg5NQEEjezMQAAAAASUVORK5CYII=","orcid":"","institution":"Birsa Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Vankadari","middleName":"Akhil","lastName":"Kumar","suffix":""},{"id":284268648,"identity":"5fab873b-4373-41bd-9cc9-954a1b6997a8","order_by":1,"name":"Niraj Kumar","email":"","orcid":"","institution":"Birsa Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Niraj","middleName":"","lastName":"Kumar","suffix":""},{"id":284268649,"identity":"b1d81373-b990-4d3b-8bca-9c6afc74f656","order_by":2,"name":"Kommineni Jagadeesh","email":"","orcid":"","institution":"Birsa Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Kommineni","middleName":"","lastName":"Jagadeesh","suffix":""},{"id":284268650,"identity":"0be8a7a0-7793-445c-9bae-7fbe4c798329","order_by":3,"name":"Arun Kumar","email":"","orcid":"","institution":"Birsa Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Arun","middleName":"","lastName":"Kumar","suffix":""},{"id":284268651,"identity":"cc56200f-1a54-49ac-92b2-3e84d7355a23","order_by":4,"name":"Chandrasekhar Mahto","email":"","orcid":"","institution":"Birsa Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Chandrasekhar","middleName":"","lastName":"Mahto","suffix":""},{"id":284268652,"identity":"f1e15e5d-f72c-4199-858b-08a98bf7bbc1","order_by":5,"name":"Hemchandra Lal","email":"","orcid":"","institution":"Birsa Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Hemchandra","middleName":"","lastName":"Lal","suffix":""},{"id":284268653,"identity":"d3ea45f2-24d5-4622-9415-5e0f07eda803","order_by":6,"name":"Binay Kumar","email":"","orcid":"","institution":"Birsa Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Binay","middleName":"","lastName":"Kumar","suffix":""},{"id":284268654,"identity":"9c5920b2-9d66-480d-a75c-ddf4a4303160","order_by":7,"name":"Ekhalaque Ahmed","email":"","orcid":"","institution":"Birsa Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Ekhalaque","middleName":"","lastName":"Ahmed","suffix":""},{"id":284268655,"identity":"69f2d723-e804-4f48-8115-1e2dda6ba533","order_by":8,"name":"Sunil Kumar","email":"","orcid":"","institution":"Birsa Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Sunil","middleName":"","lastName":"Kumar","suffix":""},{"id":284268656,"identity":"ff7f0a0a-ca9b-4f16-9c95-7d84f20f3b69","order_by":9,"name":"Pradeep Prasad","email":"","orcid":"","institution":"Birsa Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Pradeep","middleName":"","lastName":"Prasad","suffix":""},{"id":284268658,"identity":"92fdc551-b38b-462a-a2f0-156eaf729596","order_by":10,"name":"Himanshu Dubey","email":"","orcid":"","institution":"Birsa Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Himanshu","middleName":"","lastName":"Dubey","suffix":""}],"badges":[],"createdAt":"2024-03-21 18:44:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4145405/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4145405/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53581935,"identity":"70420840-999c-4642-9182-3a6a13e38151","added_by":"auto","created_at":"2024-03-27 17:38:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":5692,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"PlaceholderImage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4145405/v1/5819176318dba7cd09619aa1.png"},{"id":53581934,"identity":"0bcb3a0d-7e73-41f1-ac80-e0d99b63ecf2","added_by":"auto","created_at":"2024-03-27 17:38:55","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":64685,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHeatmaps for 15 mustard genotype performance across 4 test-environments for seed yield per plant\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4145405/v1/4f13a9532200de000d744881.jpg"},{"id":53581938,"identity":"b4de31ff-3dfa-4394-a394-fefb29285d03","added_by":"auto","created_at":"2024-03-27 17:38:55","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":68274,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHeatmaps for 15 mustard genotype performance across 4 test environments for Oil%\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4145405/v1/ef6d16d5c627380706171b07.jpg"},{"id":53583320,"identity":"9f1aa5dd-f842-4f26-ad2f-317e0aac99d4","added_by":"auto","created_at":"2024-03-27 17:46:55","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":54963,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAMMI biplot 1 and Biplot 2 of Seed yield per plant\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4145405/v1/da5ec7267db1ee0b6edfcce0.jpg"},{"id":53581941,"identity":"0b41c1f3-b2fd-4ab9-a1f1-b8cdcebe32b1","added_by":"auto","created_at":"2024-03-27 17:38:55","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":59628,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAMMI biplot 1 and biplot2 for Oil %\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4145405/v1/29277761ddf40fdedb2f69c5.jpg"},{"id":53581940,"identity":"6e9fd303-b8f5-411a-bd71-542b5586102a","added_by":"auto","created_at":"2024-03-27 17:38:55","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":76726,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWhich won where plot (A) Seed Yield per Plant (B) Oil percentage (%)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4145405/v1/f9fbb6fddbf212cbb3c2b902.jpg"},{"id":53583321,"identity":"ec79cfed-5344-4892-99d5-23a11a9238f9","added_by":"auto","created_at":"2024-03-27 17:46:55","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":78893,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMean vs Stability plot (A) Seed Yield per Plant (B) Oil percentage (%)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4145405/v1/c6d2eeb656ca7cbd173cdf7b.jpg"},{"id":53581942,"identity":"234bb688-481f-4f20-88d6-8131c55664f9","added_by":"auto","created_at":"2024-03-27 17:38:55","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":59288,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRanking of environments plot (A) Seed Yield per Plant (B) Oil percentage (%)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4145405/v1/d793e8a212b23593d029050d.jpg"},{"id":53581943,"identity":"0ff63264-94f2-4863-8455-3fdb1327dbc5","added_by":"auto","created_at":"2024-03-27 17:38:55","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":50778,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRanking of environments plot (A) Seed Yield per Plant (B) Oil percentage (%)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4145405/v1/a145c322c0d16ea511fb1d66.jpg"},{"id":53584006,"identity":"01fe3603-c72f-4a3f-a10a-db26acbff1fb","added_by":"auto","created_at":"2024-03-27 17:54:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":860566,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4145405/v1/93bc1e3f-3402-401f-b9f0-5a575e08452e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Stability Assessment for Improved Mustard Production in Ecologically Diverse Regions of Jharkhand: Insights from AMMI and GGE","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFeeding the rapidly growing global population amidst escalating climate adversities is a paramount task facing the agricultural community. Besides food grains, oil seeds hold greater importance by ranking fourth among all the crops. Among the several oil seed crops, mustard is an important edible oil seed crop belonging to the Brassicaceae family with a diploid genome 2n\u0026thinsp;=\u0026thinsp;36. It is the world\u0026rsquo;s most important crop in-terms of edible oil after soya bean and oil palm. Mustard possesses huge potential in-terms of health benefits. The consumption of mustard oil can overcome obesity (Chhajed et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The antioxidant rich composition of mustard seed can provide the protection for biochemical from oxidative damage (Dua et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Moreover, diets based on mustard oil have been shown to elicit hypoglycaemic effects through enhanced insulin activity (Sukanya et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The extensive cultivation of rapeseed and mustard in India underscores their significance, covering an area of 7.9\u0026nbsp;million hectares with a productivity of 1.49 tons per hectare, resulting in a total production of 11.9\u0026nbsp;million tons. However, the growing needs and demands for oilseeds, including mustard, remain unmet. But needs and demand of oil seeds along with mustard have not been met. The further increase in edible oil imports to 16.47\u0026nbsp;million tons during the year 2022\u0026ndash;2023 emphasizes the greater need to bolster oil seed production in our country. This enhanced the immediate need for enhancing the production of oil seeds crop including mustard.\u003c/p\u003e \u003cp\u003eThe central objective of plant breeding to improve the yield levels is significantly challenged by the polygenic nature of the trait. The production levels of mustard are witnessing huge fluctuations due to lack of adaptability to the wide range of agroclimatic conditions existing in various agro-climatic regions. The differential response of the crop cultivars in differential environments was the result of genotype x environment interaction (Kang, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). The analysis of variance can provide a preliminary understanding on existence of impact of the environment and its interactions. In order to develop varieties with stable yields irrespective of environment, there is a need to delineate the environmental impacts on the genotypic performance which is possible by the application of stability analysis models. Several statistical concepts like eco-valence-based stability (Wricke, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1965\u003c/span\u003e), analysis models like Eberhart and Russel (Eberhart \u0026amp; Russell, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1966\u003c/span\u003e), Freeman and Perkins (Freeman \u0026amp; Perkins, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1970\u003c/span\u003e), Finlay and Wilkinson model (Finlay \u0026amp; Wilkinson, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1963\u003c/span\u003e), AMMI and GGE are used in mustard (Abu et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Chauhan et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Tadesse et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and several other oils seed crops (Kebede, Worku, Jifar, et al., 2023; Khan et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Memon et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) for identifying the stable genotypes. Among these, the methods AMMI and GGE are being used to identify stable genotypes across environments which can aid in crop improvement of mustard. The current study for identification of existing diversity and associations among the genotypes and most stable genotypes across locations can improve mustard production levels.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e \u003cstrong\u003ePlant Materials\u003c/strong\u003e \u003cp\u003eIn the current study, 15 genotypes of mustard (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) including hybrids, open-pollinated varieties, and locally and nationally released varieties are collected from various sources.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003e15 genotypes of Brassica Juncea evaluated at four locations of Jharkhand.\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS. No\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eName of\u003c/p\u003e \u003cp\u003eGenotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSHIVANI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDepartment of GPB, BAU, Ranchi\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45S35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePHI Seeds\u003c/p\u003e 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\u003cp\u003eNRCHB-101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDRMR, Bharatpur\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e9.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45S46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePHI Seeds\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e10.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePUSA BOLD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIARI Pusa New Delhi\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e11.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePA-5232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBAYER Bio-Science\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e12.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKRANTHI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCSA, Kanpur\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e13.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45S47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePHI Seeds\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e14.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePA-5210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBAYER Bio-Science\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e15.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBAUM-08-18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDepartment of GPB, BAU, Ranchi\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eExperimental Location and Design\u003c/strong\u003e \u003cp\u003eThe genotypes were planted using a randomized block design with three replications across four distinct agro-climatic regions of Jharkhand named, Ranchi Agricultural College farm in Ranchi (latitude 23.23\u0026deg;N, longitude 85.23\u0026deg;E), ZRS Chainki (latitude 22.12\u0026deg;N, longitude 83.20\u0026deg;13\u0026acute; E), ZRS Dumka (latitude 22\u0026deg;33\u0026acute;N, longitude 86\u0026deg;30\u0026acute;E), and ZRS Darisai (latitude 24\u0026deg;16\u0026acute;N, longitude 87\u0026deg;15\u0026acute;E) during the rabi season of 2020-21. Each genotype was allocated to 10-row plots, each measuring 5 meters in length. The spacing between rows and plants was maintained at 30cm and 10cm, respectively, and standard agronomic practices were followed.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eData Collection\u003c/strong\u003e \u003cp\u003eThe trait yield per plant (g) was recorded for five randomly selected plants, and the average values were considered for analysis. The oil percentage (%) was estimated using near-infrared spectroscopy (NIRS) with the Pfeuffer-GmbH GRANOLYSER. Seed samples used for oil percentage (%) measurement were poured into a beaker up to a quantity of 600 ml, and the oil content for each sample within a replication was measured twice, with average values recorded. The wavelength range utilized was 950\u0026ndash;1540 nm, and the process oif measurement took approximately 30 seconds.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eStatical analysis\u003c/strong\u003e \u003cp\u003eThe data analysis was conducted utilizing R software (version 4.3.2). The Bartlett test was applied using GenStat version 22.0 to assess homogeneity of variances. Pooled analysis of variance (ANOVA) was executed employing the agricolae package (version 1.3-7) to evaluate the significance of genotype and environmental effects. The heat maps of genotype indicating performances across environments were generated with the tidyverse (version 2.0.0) and ggplot2 (version 3.4.4) packages. Furthermore, AMMI analysis was performed utilizing the metan (version 1.18.0) package, combined with ggplot2 (version 3.3.4) for graphical visualization, to elucidate genotype by environment interaction effects.\u003c/p\u003e \u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cp\u003eThe Bartlet test's non-significance (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) has proven the presence of error variance homogeneity among the four test environments and pooled analysis was carried out further. The ANOVA for seed yield plant\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003c/sup\u003e and oil percentage (%) is presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The profound impact of genotypes, environments, and genotype x environment on seed yield plant\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003c/sup\u003e and several other related traits was evident from a pooled analysis of ANOVA which was in agreement with several existing studies (Chauhan et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Kumar et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sagolsem et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013a\u003c/span\u003e; Sah et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The lack of stable performance in the genotypes enhanced the need for stability studies to decode the genotype x environmental interactions and identify the most stable performing genotypes among the test locations.\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eAdditive main effects and Multiplicative interaction model (AMMI):\u003c/h2\u003e \u003cp\u003eThe AMMI analysis for grain yield and oil % has revealed that the significant impact of environment, genotypes and environment x genotype components (Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The significant impact of environment and genotype X environment interaction was reported in mustard (Sagolsem et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2013b\u003c/span\u003e) and several other oil seed crops(Amala Balu et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Kona et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In case of seed yield per plant, the components of environment, genotypes and GXE account for 64.1 %, 10.8 % and 15.5 % respectively. Like seed yield per plant, the ifluenceof componets followed the same order in the case of oil percentage i.e., environment, genotypes and environment x genotype components account for 37.4%, 11.37% and 21.55% respectively. Compared to genotypes, the greater impact of environment and environment x genotype interaction on traits of importance i.e., seed yield per plant and Oil percentage (%) enhanced the need for stability studies.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAmmi analysis Table for Seed Yield per Plant\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSum Sq\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean Sq\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePr (\u0026gt;\u0026thinsp;F)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eProportion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAccumulated\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eENV\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e492.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e164.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00000463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e64.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eREP(ENV)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0000155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGEN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.46E-17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGEN: ENV\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e118.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.07E-15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePC1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e56.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e56.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePC2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e85.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePC3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResiduals\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e763.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAmmi analysis Table for Oil percentage (%)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSum Sq\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean Sq\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePr (\u0026gt;\u0026thinsp;F)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eProportion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAccumulated\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eENV\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e127.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42.342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.60E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e37.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eREP(ENV)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.98E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGEN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.67E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGEN: ENV\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.58E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e21.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePC1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e68.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e68.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePC2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.70E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e89.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePC3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.29E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResiduals\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e21.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e338.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.8654967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eAMMI Biplot 1:\u003c/h2\u003e \u003cp\u003eThe Ammi-biplot 1 is the plot resulted from the main effects of grain yield and IPCA 1 scores of genotypes and environments against each other. The first two principal components together have explained 85.2% and 89.5% of the variation in seed yield per plant and oil % respectively, which account greater percent of genotype X environment interactions. The outcome of greater variation explained by first two PCs was in agreement with existing study (Kona et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The ordinate divides the biplot into four sections of which the sections towards right side indicate higher trait performance compared to mean. From Figs.\u0026nbsp;1 and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the dispersion of genotypes across four quadrants indicates differential response of genotypes in-terms of seed yield per pant and oil % across study locations. The genotypes 45S47, Kesari gold, BAUM-09-12-1, 45S46, PA-5210, 45S35, DRMR-1153-12, BAUM-08-18 fallen under quadrants located right side indicates superior performance for seed yield per plant (Fig.\u0026nbsp;1). Similarly, genotypes DRMR-1153-12, DRMR-CI-70, NRCHB-101, BAUM-08-18, PUSA BOLD, BAUM-09-12-1, Shivani, Kesari Gold and 45S46 observed under right side quadrants are superior in-terms of oil % (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Among the test environments, Chiyanki in case of seed yield per plant and the environments Darisai and dumka in-case of oil % has fallen under the quadrants with higher grain yield than the mean.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eAMMI Biplot 2:\u003c/h2\u003e \u003cp\u003eThe biplot 2 resulted from plotting IPCA 1 and IPCA 2 scores against each other which can divulge the genotype and environment interactions. In case of both seed yield per plant and oil % the environment 1 vector was shorter when compared to others (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;4). The environment vector which much proximity to zero axis i.e., environment 1 for both seed yield per plant and oil % would be ideal for the selection of the most stable genotypes. The genotypes Kranthi, PA-5232 and BAUM-09-12-1 for seed yield per plant and BAUM-08-18, Shivani, DRMRCI-70 and Pusa Bold for oil % are more proximate to origin (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;4). The proximity of above genotypes in respective traits represent the stable performance across the environments when compared to genotypes that dispersed across biplot way from origin. The genotypes 45S47 and Kesari gold for Env 2, Shivani and RH-0919 for Env 4 and PA-5210 for Env 1 and NRCHB-101 for 3 and BAUM-0818 for env1 are specifically adapted in terms seed yield per plant performance. Similarly, the genotypes Kranthi for env 2, 45S46 and 45S47 for env 3, BAUM-09-12-1 for env 4 and DRMR-1153-12 for env 1 most adaptable genotypes for oil %\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eGGE Biplot (Which-won-where):\u003c/h2\u003e \u003cp\u003eThe principal components PC1 and PC2 together accounted 78.5% of variation which explains the combined impact of genotype and GXE interactions. From the figure, the polygon has been divided into 7 components with 2,1,3,2,2,2 and 3 genotypes in each component respectively. The genotypes BAUM-08-18 in Kanke, Shivani in Chiyanki, G3 in Dumka and DRMR-1153-12 in Darisai are highly stable and superior in terms of yield. The genotypes lying at the vertex of the polygon are superior and best in the particular environment falling in the respective sectors (Yan \u0026amp; Tinker, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The genotypes 45S35, 45S46, BAUM-09-12-1, PA-5210, NRCHB-101, RH-0919, KRANTHI and DRMRCI-70 are less responsive to the environment. The impact of the environment is less profound on the genotypes which are positioned inside the polygon (Khan et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The genotypes SHIVANI, PA-5232 and Pusa Bold are poor performers across all the environments. The genotypes positioned at the vertex of the polygon positioned in sector which is unoccupied by any of the environment indicates its low performance for a particular trait (Ruswandi et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The environments falling in the same sector of the polygon are represented as the mega environment (Choudhary et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). As the four environments are dispersed across different sectors, no mega environment has been identified. Similarly in case of oil%, the genotypes DRMR-1153-12 and BAUM-08-18 are the vertex genotypes in both the environments Ranchi and Darisai indicating their superiority in the respective environments.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMean vs Stability:\u003c/h2\u003e \u003cp\u003eThe mean vs stability bi-plot (fig) helps us to evaluate the genotypes based on the mean performance and stability. The two lines i.e., abscissa (horizontal) and ordinate (vertical) constitute the biplot graph and the arrow mark on the abscissa indicates the order of the well performing genotypes for the trait evaluated. The ordinate groups the genotypes i.e., which falling on the right and left of it are classified as high and low yielding respectively. The projection length of genotypes from abscissa is the measure of their stability. Based on the above two considerations and from the fig, the genotypes for seed yield per plant and oil percentage (%) are classified into four groups i.e., high yield and highly stable, high yielding but less stable, less yielding but highly stable and less yielding less stable are tabulated (table). Among the several genotypes under study, PA-5210 is most stable and high yielding in-terms of seed yield per plant. In case of oil percentage (%), BAUM-08-18 is the most stable and high performing genotype.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClassification of genotypes based on Mean vs Stability Plot\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGenotypes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eSeed Yield per plant\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eOil percentage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh yielding\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow yielding\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh yielding\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLow yielding\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHighly stable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45S35,45S46 and BAUM-09-12-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePA532,\u003c/p\u003e \u003cp\u003eKranthi and DRMRCI-70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePusa bold, BAUM-08-19, NRCHB-101 and DRMRCI-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePA-5232, PA-5210\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess stable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePA-532, Kranthi and RH-0919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eShivani, Pusa bold\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDRMR-1153-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45S46, 45S47, Kranthi\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eRanking of Environments:\u003c/h2\u003e \u003cp\u003eThe discriminative power and representativeness of environment was basis of the ranking of the environments. From the fig, in case of seed yield per plant, the environment dumka is closer to inner circle followed by darisai, ranchi and chiyanki. Similarly in case of oil %, the environment ranchi was closer to inner circle followed by darisai, dumka and Chiyanki. Based on the biplot ranking of environments, the environments closer to inner concentric circle are most desirable in terms of ability to discriminate the genotypes and to be representative of all the environments considered(Kebede, Worku, Feyissa, et al., 2023). The environments dumka and ranchi ae most ideal environments for testing and evaluating the genotypes for seed yield per plant and Oil % respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003eRelationship among Environments:\u003c/h2\u003e \u003cp\u003eThe environmental vectors represented in biplot (Fig) provides information about relationship among the environments studied. In case of seed yield per plant, the cosine angle among the environments Ranchi and Darisai, Ranchi and Dumka, Darisai and Dumka, Chiyanki and Dumka was less than 90 (acute) and greater than 90 (obtuse) in case of Ranchi and Chiyanki and Chiyanki and Darisai. The length of Ranchi vector was smaller when compared to others. The cosine angle among the environments Ranchi and Chiyanki, Ranchi and Darisai and Ranchi and Dumka, Chiyanki and Darisai was less than 90 and the angle between the environments Dumka and Chiyanki was obtuse. The acute angle between the environmental vectors indicates the positive correlation and the obtuse angle is indicative of the inverse relationship among them (Yan \u0026amp; Tinker, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The environments Ranchi and Darisai, Ranchi and Dumka, Darisai and Dumka, Chiyanki and Dumka in case of seed yield per plant and environments Ranchi and Chiyanki, Ranchi and Darisai and Ranchi and Dumka, Chiyanki and Darisai for oil % are positively correlated. The environments Chiyanki and Ranchi, Chiyanki and Darisai in seed yield per plant and Dumka and Chiyanki are negative correlated.\u003c/p\u003e \u003cp\u003eThe perpendicular alignment of environmental vectors signifies their independence, while varying lengths suggest degrees of correlation, with similar lengths indicating stronger associations (Haruna et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The strong association between the environments is very useful in saving huge resources by avoiding the need of evaluating genotypes at multiple environments.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe prominence of environmental influence on performance of mustard genotypes was evident. The AMMI and GGE biplot has provided the key insights on the performance and stability of the stability of the genotypes. The substantial portion of the variation among the performance of the genotype was due to the environment and its interactions. The AMMI and GGE has identified the most and performing genotypes for across and the specific locations. The information on the environments that can distinguish superior performing mustard genotypes for seed yield per plant and oil % are identified. The genotypes BAUM-09-12-1 and Pusa Bold are high yielding and most stable for seed yield per plant and oil % respectively. The environment Ranchi has been commonly identified as core site for testing for Oil % by AMMI and GGE and Dumka has been identified test environment to differentiate the genotypes for seed yield per plant GGE. This information can contribute to the promotion of mustard cultivation across the Jharkhand.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting Interests:\u003c/h2\u003e \u003cp\u003eThe authors declare there are no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eNiraj Kumar, Chandrasekhar Mahto, Hemchandra Lal, Binay Kumar and Himanshu Dubey has conceived and planned the experiments. Akhil kumar has carried out the experiment and took lead in manuscript preparation. Ekhalaque Ahmed, Sunil Kumar, Pradeep Prasad are involved the carrying out experiment. Kommineni Jagadeesh has involved in data analysis and manuscript preparation. Arun kumar has provided critical inputs and helped in shaping manuscript. The results were discussed among all the authors and were in agreement with each other.\u003c/p\u003e\u003ch2\u003eData Availability:\u003c/h2\u003e \u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbu, M., Mengistu, B., \u0026amp; Molla, T. (2022). Genotype by Environment Interaction and Stability Analysis in Ethiopian Mustard (Brassica Carinata A Braun) Using AMMI Biplot and Stability Parameters. \u003cem\u003eAmerican Journal of Life Sciences\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(3), 39\u0026ndash;44.\u003c/li\u003e\n\u003cli\u003eAmala Balu, P., Sumathi, P., Ibrahim, S. M., \u0026amp; Kalaimagal, T. 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Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: a pooled analysis of 1201 population-representative studies with 104 million participants. \u003cem\u003eThe Lancet\u003c/em\u003e, \u003cem\u003e398\u003c/em\u003e(10304), 957\u0026ndash;980. https://doi.org/10.1016/S0140-6736(21)01330-1\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-ecology-and-evolution","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"evob","sideBox":"Learn more about [BMC Ecology and Evolution](http://bmcevolbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/evob/default.aspx","title":"BMC Ecology and Evolution","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Genetic variability, AMMI, GGE and stability","lastPublishedDoi":"10.21203/rs.3.rs-4145405/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4145405/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe present study investigates the genetic variability and stability of 15 mustard (Brassica juncea) genotypes across four diverse locations in Jharkhand, employing a randomized block design with three replications. Significant differences among the genotypes are observed, with environmental factors and their interactions exerting a considerable influence. Utilizing the AMMI and GGE biplot methods, the study delves into the intricate interactions affecting economically vital traits such as seed yield per plant and oil percentage. The combined effect of environment and interaction explains a substantial portion of the observed variation of 79.6 and 58.9% on seed yield per plant and oil % respectively. The first two principal components together explained larger portion of 85.2% and 89.5% of the GXE variation of seed yield per plant and oil % respectively. The AMMI analysis had identified that, the genotypes Kranthi, PA-5232 and BAUM-09-12-1 for seed yield per plant and BAUM-08-18, Shivani, DRMRCI-70 and Pusa Bold for oil % are stable performers. The GGE biplot analysis and AMMI have commonly identified BAUM-09-12-1 and Pusa Bold as high yielding and most stable for seed yield per plant and oil % respectively. The results of AMMI identified ranchi as most ideal environment for selection of genotypes for both seed yield per plant and oil%, but GGE differs in-terms with Ranchi as ideal only for oil% and dumka for seed yield per plant. The availability of the above information of genetic variability and stability of genotypes for seed yield per plant and oil % can aid improving mustard production levels and self-sufficiency in edible oils.\u003c/p\u003e","manuscriptTitle":"Stability Assessment for Improved Mustard Production in Ecologically Diverse Regions of Jharkhand: Insights from AMMI and GGE","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-27 17:38:50","doi":"10.21203/rs.3.rs-4145405/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-03-26T21:55:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-23T04:39:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-23T04:39:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Ecology and Evolution","date":"2024-03-21T18:41:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-ecology-and-evolution","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"evob","sideBox":"Learn more about [BMC Ecology and Evolution](http://bmcevolbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/evob/default.aspx","title":"BMC Ecology and Evolution","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8425910b-8258-4345-bdad-ba97bc249b39","owner":[],"postedDate":"March 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-04-03T11:57:02+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-27 17:38:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4145405","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4145405","identity":"rs-4145405","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}