Evaluation of finger millet genotypes for stability using parametric and non-parametric methods in India (original) (raw)
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Electronic Journal of Plant Breeding, 2018
A study of phenotypic stability of 13 finger millet genotypes was conducted to assess genotype-environment interaction (GEI) and identify stable finger millet (Eleusine coracana (L.) Gaertn. subsp. coracana) genotypes for grain yield across four diverse locations in India. Both parametric and non-parametric stability statistics were used to identify stable finger millet genotypes. The parameters Wi 2 , σi 2 , Si (1) , Si (2) identified similar stable genotypes, while different stable genotypes were identified by other measures. High correlation among non-parametric and parametric measures showed that these measures can be used alternatively. Only two stability measures, Ysi and YSI showed significant association with mean grain yield and Ysi was better choice for screening of genotypes for both yield and stability. The stable high yielding genotypes PPR 2773, VL 368, KOPN 942, VR 988, TNAU 1214 and GPU 45 can be deployed or included in breeding program for enhancing the finger millet productivity.
Graphical analysis of genotype by environment interaction of Finger millet grain yield in India
Electronic Journal of Plant Breeding, 2018
Finger millet (Eleusine coracana (L.) Gaertn. subsp. coracana) is an important food-grain in semi-arid, hilly tribal areas of India and Africa for subsistence farming. GGE biplot techniques were applied for the assessment of stability and patterns of Genotype by Environment Interaction (GEI) in elite finger millet genotypes grown in four different locations. The combined ANOVA for grain yield of thirteen finger millet cultivars at four environments showed that Environments (E), Genotypes (G) and GEI were highly significant. The partitioning of GEI sum of squares showed that first and second IPCA axis accounted for 64.1% and 28.1% of the interaction sum of squares for GGE analysis. The biplot analysis grouped the four environments into two mega environments with VL 368 and VR 988 as winning genotypes. The genotype VL 368 was found to be an ideal genotype in terms of high yield and stability followed by KOPN 942, PPR 2773, TNAU 1214, VR 988 and VL 369 as desirable genotype. Among environments, E1 and E3 were the most interactive environments while, E2 and E4 showed little variation in genotypes relative ranking.
African Journal of Agricultural Research, 2018
An experiment was conducted to study the adaptability and genotype × environment interaction of finger millet varieties in the north eastern part of Ethiopia. Eight finger millet varieties and a local check were tested at Sirinka, Kobo, and Jari in 2013 and 2014 cropping season. The experiment was laid out in a randomized complete block (RCB) design with three replicates. The result showed that the year 2013 was relatively better than 2014 for finger millet yield. Variety Bareda ranked first in terms of yield at Sirinka both in 2013 and 2014 (SR13 and SR14), and Kobo in 2013 (KB13). Variety Tadesse ranked first at Jari in 2013 (JR13); however, both the local check and Gute ranked first at Jari in 2014 (JR14). Except at JR14, the local check ranked second in all the environments. The Additive Main-effect and Multiplicative Interaction (AMMI) analysis showed that the best fit model was AMMI1 and it explained 68.54% of the genotype × environment interaction. Genotypes Bareda, the local check and Gute had higher grain yield in that order. Similarly, environments SR13, JR13 and KB13 had above average grain yield. Varieties Tadesse and Padet had small interaction effect; however, Bareda and Gute exerted relatively higher interaction effect. Similarly, environment SR13 contributed minimum interaction effect; whereas KB13 and JR13 contributed higher interaction effect. Genotype and genotype × environment (GGE) biplot identified the local check, Bareda and Gute as more desirable varieties. Based on the overall performance and adaptability of the finger millet varieties across environments, Bareda could be recommended for production at Sirinka and Kobo, whereas the local check could still be used at Jari.
American Journal of Plant Sciences, 2011
Finger millet is one of the most neglected and underutilized crops worldwide, yet an important food cereal for millions of poor farmers in Africa. An experiment was carried out to determine adaptation range of diverse set of finger millet accessions and identify superior types with excellent yield potential for use as cultivar or as germplasm source for future breeding endeavors. A total of 44 indigenous accessions selected in previous evaluations and two check varieties were tested in two sets (mixed and colored) each containing 22 entries in a total of 11 environments between 2004 and 2008 seasons. Data were collected on grain yield, days to flowering, and plant height. The result showed that 2.5%, 79.1% and 18.3% of the total sum of squares in the mixed set and 2.1%, 86.9% and 11.0% in the colored set was attributed to genotype, environment, and genotype × environment interaction (GEI) effects, respectively. Furthermore, 54.6% and 46.19% of the GEI sum of squares in the mixed and in the colored set, respectively, were contributed by the first two interaction principal component axes (IPCA1 and IPCA2). A white seed accession (Acc. 203572) from the mixed set and three other accessions (Acc. 229469, Acc. 203410 and Acc. 203539) from the colored set were most stable and also had above average mean grain yield across environment and thus are recommended for release as cultivars to improve finger millet production in these environments.
Electronic Journal of Plant Breeding
Finger millet (Eleusine coracana (L.) Gaertn. subsp. coracana) production has become stagnant over the years and one of the possible ways to increase the production can be spread of widely adaptable high yielding cultivars. Five national finger millet cultivars were grown in randomized complete block design at ICAR-Vivekananda Institute of Hill Agriculture for six consecutive years to evaluate the grain yield stability. The grain yield data were subjected to AMMI and GGE biplot techniques for assessing the stability and patterns of GE interaction in finger millet National cultivars. The combined ANOVA showed that finger millet grain yield was significantly affected by environment, which explained 54.67% of the total treatment (G+E+GE) variation, whereas the G and GEI accounted for 10.38% and 34.96%, respectively. The partitioning of GEI sum of squares using AMMI analysis indicated that the first two PCAs were highly significant. The first IPCA axis (IPCA1) accounted for 50.3% of the G×E interaction sum of squares. The second IPCA axis accounted for 38.2% of the interaction sum of squares. Both represented a total of 88.5% variation. AMMI 1 biplot indicated the general adaptation of genotype HR 374 across the environments, whereas the other genotypes showed specific adaptation to one or other environments. GGE-biplot graphical analysis further confirmed the results and revealed that HR 374 as an ideal genotype in terms of high yield and stability followed by RAU 8 as desirable genotype. In our research, both of AMMI and biplot models were successful in assessing the performance of genotypes and the selection of best genotype was identical in both of them.
Stability analysis of finger millet genotypes under the hilly regions of Nepal
2019
The aim of this study was to identify stable and high yielding genotypes under various environments and years in different hilly regions of Nepal. Five finger millet genotypes along with farmer’s variety (Local check) were tested under command areas of five different stations namely, Hill Crops Research Program (HCRP), Dolakha, National Ginger Research Program (NGRP), Salyan, Agricultural Research Station (ARS), Dailekh, ARS, Surkhet and Regional Agricultural Research Station (RARS), Kaski during 2016 and 2017 winter season under rainfed condition. The experiment was conducted using Randomized Complete Block Design with two replications under farmer’s field condition. The genotype x environment (GxE) interaction for grain yield was significant. The genotypes KLE-236 (2.37 t/ha), KLE-158 (2.32 t/ha) and DR-2 (2.02 t/ha) were found higher sensitive to environment and produced the higher mean grain yield across the locations. Joint regression analysis showed that genotypes KLE-236, KLE...
Stability Analysis for Grain Yield Attributing Traits in Finger Millet
Andhra Agricultural Journal , 2020
Stable performance of genotype in different environments is highly considered for development and release of new high yielding varieties. In the present study, thirteen advanced finger millet genotypes along with one local check were evaluated at three locations to identify stable and high yielding genotypes. None of the genotypes showed stable performance for all the traits studied. Linear component of genotype x environment (G x E) reaction was significant for number of productive tillers and grain yield ha-1 revealing the differential reaction of genotypes tested in different environments for these traits. Among the tested genotypes, PPR 1041 recorded average stability for number of productive tillers per plant indicating the wide adoptability of this genotype for number of productive tillers per plant. Average stability for grain yield was found in VR 990 which revealed the wide adaptability of the genotype across different locations.
International Journal of Plant and Soil Science, 2022
The fifty-finger millet (Eleusine coracana) genotypes used in the current experiment were examined in four different environments: E1 and E2 at the Student Research Farm at the C.S.A.U.A.&T. Kanpur, and E3 and E4 seeded at the Research Farm in Daleep Nagar, Kanpur. The genotypes were assessed using a randomised block design with three replications to determine genetic variability for the following traits: days to 50% flowering, days to maturity, plant height (cm), number of productive tillers per plant, number of fingers per ear, length of finger (cm), finger width (cm), ear head width (cm), ear head length (cm), ear head weight (g), ear head weight (g), straw yield per plant (g), harvest index (%), 1000 grain weight (g), protein content (%) and grain yield per plant (g). This experiment revealed low genotypic coefficient of variation (GCV) and phenotypic coefficient of variation (PCV) for days to 50% flowering, days to maturity, ear head width across all conditions, and protein content across all environments with the exception of E2. High levels of GCV and PCV were found in the ear head weight, straw yield per plant, 1000 grain weight, and grain yield per plant. In every context, the magnitude of GCV was often lower than the corresponding PCV. Plant
Agronomic Performance and Correlation Analysis of Finger Millet Genotypes (Elusine Corocana L.)
Malaysian Journal of Sustainable Agriculture
Considering the context of climate change and food security issues of the poor, marginalized and vulnerable farmers; there is urgent need of characterization of the traits and its correlation in the different genotypes of finger millet for development of elite variety in Nepal. A field research was carried out at agronomy field at hill crop research program (HCRP), kabre, Dolakha from June to November, 2017in order to identify the phenotypic variability of the trait in different Nepalese landraces and create to promote the production and stability of neglected crops, finger millet. The field experiment was conducted in random complete block design with two replications. The result revealed that the finger millet genotypes showed the significant differences for days to 50 % heading, plant height, plant stand per square meter, bearing head per square meter, number of finger per head, thousand grain weight and grain yield. The genotypes ACC#513 (3.68 t/ha) fallowed by ACC#2303(3.65t ha-1), ACC#2275(3.57t ha-1) and ACC#5434 (3.39 t ha-1) produces highest grain yield. Correlation analysis revealed that plant height fallowed by plant stand per square meter, bearing head, number of finger per head and straw yield with minimum lodging percentage were most yield determinative traits and simultaneous selection for these traits might brining an improvement in finger millet grain yield.