scholarly journals Genotype X Environment Response of ‘Matooke’ Hybrids (Naritas) to Pseudocercospora fijiensis, the Cause of Black Sigatoka in Banana

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1145
Author(s):  
Janet Kimunye ◽  
Kennedy Jomanga ◽  
Anthony Fredrick Tazuba ◽  
Evans Were ◽  
Altus Viljoen ◽  
...  
Keyword(s):  
Control Strategy ◽  
Environment Interaction ◽  
Black Sigatoka ◽  
Breeding Programs ◽  
Genotype X Environment ◽  
Progress Curve ◽  
Resource Poor ◽  
Costs Of Disease ◽  
Pseudocercospora Fijiensis ◽  
Over Time

Growing bananas resistant to Pseudocercospora fijiensis, the cause of black Sigatoka, is the preferred disease control strategy for resource-poor farmers. Banana breeding programs in east Africa have developed 27 Matooke hybrids (commonly known as NARITAs) with higher yields than local landraces. To assess the response of NARITA hybrids to P. fijiensis, 22 hybrids were evaluated under natural field conditions in four locations—Kawanda and Mbarara in Uganda, and Maruku, and Mitarula in Tanzania—between 2016 and 2018 for three crop cycles. Black Sigatoka was visually assessed and the area under the disease progress curve calculated for each plant over time. Significant differences (p < 0.001) were observed between genotypes, environments, and their interaction. The highest contributor to black Sigatoka severity (39.1%) was the environment, followed by the genotype (37.5%) and the genotype Χ environment interaction (GEI) (23.4%). NARITA 2, 7, 14, 21 and 23 were resistant and the most stable hybrids across locations. If other attributes such as the yield and taste are acceptable to end-users, these hybrids can be released to farmers in the region to replace highly susceptible landraces. Mitarula was identified as an ideal site for evaluating banana against black Sigatoka and should be used as a representative location to minimize costs of disease evaluations.

scholarly journals Selection of Diploid and Tetraploid Banana Hybrids Resistant to Pseudocercospora fijiensis

Agronomy ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2483
Author(s):  
Zalmar Santana Gonçalves ◽  
Anelita de Jesus Rocha ◽  
Fernando Haddad ◽  
Vanusia Batista de Oliveira Amorim ◽  
Claudia Fortes Ferreira ◽  
...  
Keyword(s):  
Quantitative Resistance ◽  
Severity Index ◽  
Black Sigatoka ◽  
Sources Of Resistance ◽  
Breeding Programs ◽  
Total Period ◽  
Progress Curve ◽  
Evaluation Time ◽  
Pseudocercospora Fijiensis ◽  
Selection Of

Black Sigatoka, a disease caused by the fungus Pseudocercospora fijiensis, can lead to the complete loss of banana and plantain production in the absence of chemical control. The development of resistant cultivars is the focus of many banana breeding programs and is an alternative to the use of fungicides. In order to define a refined method of selection in genetic breeding programs, this study evaluated 23 improved diploids, seven tetraploids, and two commercial cultivars in the presence of P. fijiensis. Four selection criteria were considered: means of the disease severity index (ID) and area under the disease progress curve (AUDPC) estimated over the total period of the experiment, only in summer, only in winter, and the emission and harvesting of bunches. The selection of genotypes was more effective in the winter, and the evaluation of four leaves no. 3 emitted after six months of growth was efficient at differentiating the resistant and susceptible genotypes. For the improved diploids and tetraploid hybrids, DI varied from 0.0 to 48.8 and from 15.1 to 63.5, respectively, and the AACPD for the improved hybrids and tetraploid hybrids varied from 0.0 to 2439.5 and 1000.2 to 3717.7, respectively. The tetraploid hybrid of the Prata-type CNPMF0906 and the commercial cultivar, which is a hybrid of the BRS Princesa Silk type, showed quantitative resistance and can be used by banana producers. Results suggest that the guidelines adopted for the selection of genotypes resistant to black Sigatoka may include methodologies that reduce the evaluation time. In addition, new sources of resistance to the disease and the influence of its genetic inheritance in future crosses were found.

scholarly journals Adaptability and stability of maize varieties using mixed model methodology

2012 ◽  
Vol 12 (2) ◽  
pp. 111-117 ◽  
Author(s):  
Flávia Ferreira Mendes ◽  
Lauro José Moreira Guimarães ◽  
João Cândido Souza ◽  
Paulo Evaristo Oliveira Guimarães ◽  
Cleso Antônio Patto Pacheco ◽  
...  
Keyword(s):  
Mixed Models ◽  
Mixed Model ◽  
Relative Performance ◽  
Environment Interaction ◽  
Maize Breeding ◽  
Breeding Programs ◽  
Genotype X Environment ◽  
Growing Seasons ◽  
Maize Varieties ◽  
Harmonic Means

The objective of this study was to evaluate the performance, adaptability and stability of corn cultivars simultaneously in unbalanced experiments, using the method of harmonic means of the relative performance of genetic values. The grain yield of 45 cultivars, including hybrids and varieties, was evaluated in 49 environments in two growing seasons. In the 2007/2008 growing season, 36 cultivars were evaluated and in 2008/2009 25 cultivars, of which 16 were used in both seasons. Statistical analyses were performed based on mixed models, considering genotypes as random and replications within environments as fixed factors. The experimental precision in the combined analyses was high (accuracy estimates > 92 %). Despite the existence of genotype x environment interaction, hybrids and varieties with high adaptability and stability were identified. Results showed that the method of harmonic means of the relative performance of genetic values is a suitable method for maize breeding programs.

scholarly journals Pattern Analysis of Sorghum Genotype × Environment Interaction for Leaf, Panicle, and Grain Anthracnose in Mali

Plant Disease ◽  
2002 ◽  
Vol 86 (12) ◽  
pp. 1374-1382 ◽  
Author(s):  
D. E. Hess ◽  
R. Bandyopadhyay ◽  
I. Sissoko
Keyword(s):  
Pattern Analysis ◽  
Environment Interaction ◽  
Colletotrichum Graminicola ◽  
Sources Of Resistance ◽  
Breeding Programs ◽  
Field Screening ◽  
Disease Progress Curve ◽  
Disease Progress ◽  
Genotype Environment Interaction ◽  
Progress Curve

Resistance to anthracnose, caused by Colletotrichum graminicola, in sorghum was identified through field screening at two locations (Samanko and Longorola) in Mali. The occurrence and progress of anthracnose were monitored on 19 sorghum lines plus resistant and susceptible checks in the 1996 to 1998 rainy seasons. Foliar anthracnose severity was assessed at regular intervals throughout the season. Area under the disease progress curve (AUDPC) was calculated for each genotype. Anthracnose severity was also evaluated on the peduncle, rachis and glumes, panicle, and grain. For the characters under study, the site × year and site × year × line interactions accounted for the genotype × environment interactions. Pattern analysis was applied to the environment-standardized matrix of genotype × environment means to analyze these interactions and elucidate genotypic adaptation. None of the lines was completely (hypersensitive) resistant to the disease, but 12 showed high levels of stable resistance to both foliar and panicle anthracnose. Only one was moderately susceptible to both forms of the disease. In addition to identifying varieties that can be grown in zones to which they are adapted, additional genotypes were identified that can serve as sources of resistance in regional breeding programs.

Developing a sustainable Digital Health Innovation Ecosystem in a rural and resource-poor region: A monitored networking approach (Preprint)

2020 ◽  
Author(s):  
Arunangsu Chatterjee ◽  
Sebastian Stevens ◽  
Sheena Asthana ◽  
Ray B Jones
Keyword(s):  
Social Network ◽  
Network Analysis ◽  
Data Collection ◽  
Digital Health ◽  
Resource Poor Setting ◽  
Quadruple Helix ◽  
Innovation Ecosystem ◽  
Resource Poor ◽  
Quadruple Helix Model ◽  
Over Time

BACKGROUND Digital health (DH) innovation ecosystems (IE) are key to the development of new e-health products and services. Within an IE, third parties can help promote innovation by acting as knowledge brokers and the conduits for developing inter-organisational and interpersonal relations, particularly for smaller organisations. Kolehmainen’s quadruple helix model suggests who the critical IE actors are, and their roles. Within an affluent and largely urban setting, such ecosystems evolve and thrive organically with minimal intervention due to favourable economic and geographical conditions. Facilitating and sustaining a thriving DH IE within a resource-poor setting can be far more challenging even though far more important for such peripheral economics and the health and well-being of those communities. OBJECTIVE Taking a rural and remote region in the UK, as an instance of an IE in a peripheral economy, we adapt the quadruple helix model of innovation, apply a monitored social networking approach using McKinsey’s Three Horizons of growth to explore: • What patterns of connectivity between stakeholders develop within an emerging digital health IE? • How do networks develop over time in the DH IE? • In what ways could such networks be nurtured in order to build the capacity, capability and sustainability of the DH IE? METHODS Using an exploratory single case study design for a developing digital health IE, this study adopts a longitudinal social network analysis approach, enabling the authors to observe the development of the innovation ecosystem over time and evaluate the impact of targeted networking interventions on connectivity between stakeholders. Data collection was by an online survey and by a novel method, connection cards. RESULTS Self-reported connections between IE organisations increased between the two waves of data collection, with Small and Medium-sized Enterprises (SMEs) and academic institutions the most connected stakeholder groups. Patients involvement improved over time but still remains rather peripheral to the DH IE network. Connection cards as a monitoring tool worked really well during large events but required significant administrative overheads. Monitored networking information categorised using McKinsey’s Three Horizons proved to be an effective way to organise networking interventions ensuring sustained engagement. CONCLUSIONS The study reinforces the difficulty of developing and sustaining a DH IE in a resource-poor setting. It demonstrates the effective monitored networking approach supported by Social Network Analysis allows to map the networks and provide valuable information to plan future networking interventions (e.g. involving patients or service users). McKinsey’s Three Horizons of growth-based categorisation of the networking assets help ensure continued engagement in the DH IE contributing towards its long-term sustainability. Collecting ongoing data using survey or connection card method will become more labour intensive and ubiquitous ethically driven data collection methods can be used in future to make the process more agile and responsive.

scholarly journals Disclosing the Nutritional Quality Diversity of Portuguese Common Beans—The Missing Link for Their Effective Use in Protein Quality Breeding Programs

Agronomy ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 221
Author(s):  
Elsa Mecha ◽  
Sofia Natalello ◽  
Bruna Carbas ◽  
Andreia Bento da Silva ◽  
Susana T. Leitão ◽  
...  
Keyword(s):  
Heat Stress ◽  
Amino Acid ◽  
Nutritional Quality ◽  
Dietary Habits ◽  
Protein Quality ◽  
Trypsin Inhibitor Activity ◽  
Environment Interaction ◽  
Individual Amino Acid ◽  
Common Beans ◽  
Breeding Programs

The common bean (Phaseolus vulgaris L.) represents a sustainable and affordable source of protein, namely, to populations with vegetarian dietary habits. Despite the national germplasm genetic diversity, little is known about the Portuguese accessions’ nutritional and protein quality, leading to their underuse in breeding programs. To fill this gap, a representative collection (106 accessions) was cropped under two contrasting environments (traditional versus heat stress) and evaluated in terms of nutritional quality by near-infrared spectroscopy. Protein quality was assessed, under the stressful environment, considering the individual amino acid contents and the activity of trypsin inhibitors through mass spectrometry (LC-MS/MS) and spectrophotometry, respectively. On top of strong genotypic control, the nutritional composition (protein, fat, fiber, moisture and ash) was also highly influenced by the environment and by genotype × environment interaction, with a clear nutritional quality ranking change for the accessions in heat stress conditions. Classified into three clusters, the accessions from the cluster with the highest individual amino acid and protein contents also showed higher trypsin inhibitor activity (TIA). Since different levels of TIA had no translation into contrasting protein digestibility, breeders focusing on common beans’ protein quality improvement, especially under challenging warming climate conditions, may take advantage of this group of accessions.

scholarly journals Genotype X Environment Interaction of Food Barley

2015 ◽  
Vol 21 ◽  
pp. 41-48
Author(s):  
Gebremedhin Welu
Keyword(s):  
Grain Yield ◽  
Block Design ◽  
Genotype X Environment Interaction ◽  
Environment Interaction ◽  
Genotype X Environment ◽  
Genotype Effect ◽  
Environment Effects ◽  
Randomized Complete Block Design ◽  
Barley Genotypes

The objective of this experiment was to estimate the magnitude of genotype X environment interaction on grain yield and yield related traits. Twelve varieties of food barley were included in the study planted in randomized complete block design with three replications. The ANOVA of combined and individual location revealed significant differences among the food barley genotypes for grain yield and other traits. The results of ANOVA for grain yield showed highly significant (p≤0.01) differences among genotypes evaluated for grain yield at Maychew and significant (p≤0.05) differences in Korem, Alage and Mugulat. The ANOVA over locations showed a highly significant (p≤0.01) variation for the genotype effect, environment effects, genotype X environment interaction (GEI) effect and significant (p≤0.05) variation for GEI effect of yield and for most of the yield related traits of food barley genotypes. Haftysene, Yidogit, Estayish and Basso were the genotypes with relatively high mean grain yield across all locations and they are highly performing genotypes to the area. Among locations, the highest mean grain yield was recorded at Korem and it was a suited environment to all the genotypes whereas Mugulat is unfavoured one. ECOPRINT 21: 41-48, 2014DOI: http://dx.doi.org/10.3126/eco.v21i0.11903

scholarly journals Genotype x Environment Interaction and Yield-Stability Analyses of Rice Grown in Tropical Inland Swamp

2011 ◽  
Vol 39 (1) ◽  
pp. 220 ◽  
Author(s):  
Adesola L. NASSIR ◽  
Omolayo J. ARIYO
Keyword(s):  
Total Variation ◽  
Yield Stability ◽  
Genotype X Environment Interaction ◽  
Environment Interaction ◽  
Rice Varieties ◽  
Environmental Indices ◽  
Genotype X Environment ◽  
Additional Information ◽  
Gxe Interaction ◽  
The Stability

Twelve rice varieties were cultivated in inland hydromorphic lowland over a four year-season period in tropical rainforest ecology to study the genotype x environment (GxE) interaction and yield stability and to determine the agronomic and environmental factors responsible for the interaction. Data on yield and agronomic characters and environmental variables were analyzed using the Additive Main Effect and Multiplicative Interaction (AMMI), Genotype and Genotype x Environment Interaction, GGE and the yield stability using the modified rank-sum statistic (YSi). AMMI analysis revealed environmental differences as accounting for 47.6% of the total variation. The genotype and GxE interaction accounted for 28.5% and 24% respectively. The first and second interaction axes captured 57% and 30% of the total variation due to GXE interaction. The analysis identified ‘TOX 3107’ as having a combination of stable and average yield. The GGE captured 85.8%of the total GxE. ‘TOX 3226-53-2-2-2’ and ‘ITA 230’ were high yielding but adjudged unstable by AMMI. These two varieties along with ‘WITA 1’ and ‘TOX 3180-32-2-1-3-5’ were identified with good inland swamp environment, which is essentially moisture based. The two varieties (‘TOX 3226-53-2-2-2’ and ‘ITA 230’), which were equally considered unstable in yield by the stability variance, ?2i, were selected by YSi in addition to ‘TOX 3107’, ‘WITA 1’, ‘IR 8’ and ‘M 55’. The statistic may positively complement AMMI and GGE in selecting varieties suited to specific locations with peculiar fluctuations in environmental indices. Correlation of PC scores with environmental and agronomic variables identified total rainfall up to the reproductive stage, variation in tillering ability and plant height as the most important factors underlying the GxE interaction. Additional information from the models can be positively utilized in varietal development for different ecologies.

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