Genotype × environment interaction of potato chip colour

1999 ◽  
Vol 79 (3) ◽  
pp. 433-438 ◽  
Author(s):  
G. C. C. Tai ◽  
W. K. Coleman
Keyword(s):  
Regression Analysis ◽  
Storage Temperature ◽  
Environment Interaction ◽  
Marketable Yield ◽  
Environmental Index ◽  
Glucose Content ◽  
Chip Colour ◽  
Genotype Environment Interaction ◽  
Temperature Regimes

Ten potato genotypes were tested in replicated trials over three sites in New Brunswick. Five of them were tested in 1991, 1992 and 1993, and another five in 1992 and 1993. Tubers were harvested and put in storage rooms with two temperature regimes: 7 °C and 13 °C. Prior to testing glucose content and chip colour, a portion of tubers stored in 7 °C was sampled and subjected to reconditioning for 2 and 4 wk in a storage room with the temperature regime of 21 °C. High relative humidity (>80%) was maintained in all storage rooms. Glucose and colour score of chips were determined during November in each of the three years. Analysis of variance revealed genotype × environment interactions for both traits. Since variation of chip colour is controlled primarily by glucose content in potatoes, this causal relationship was used as a basis to perform path regression analysis for each of the genotypes, based on all available data of the two traits. The regression equation is composed of two terms: an average chip score over environments (µ) and a multiplicative term with a genotypic coefficient (g) and an environmental index (r); µ measures the overall chipping ability, whereas g responds to environment and thus represents chipping stability. The 10 genotypes were different from one another on estimates of both parameters. The estimates of r showed lesser differences between the three test sites than between storage-temperature regimes. Storing potatoes at 13 °C and 7 °C gave the best and worst chip-colour scores, respectively. Reconditioning at 21 °C after storing at 7 °C improved the colour performance. No clear association was observed between chip colour, specific gravity and marketable yield. Good chipping genotypes tested in the experiment, however, had lower yields than others. The present results indicate that path regression analysis is an effective method for detecting the role of glucose content in the control of the expression of chip colour. Key words: Potato, Solanum tuberosum, chip colour, glucose content, storage

Genotype–environment interaction of lovegrass forage yield in the semi-arid region of Argentina

2001 ◽  
Vol 137 (3) ◽  
pp. 329-336 ◽  
Author(s):  
M. A. IBAÑEZ ◽  
M. A. DI RENZO ◽  
S. S. SAMAME ◽  
N. C. BONAMICO ◽  
M. M. POVERENE
Keyword(s):  
Regression Analysis ◽  
Arid Region ◽  
Interaction Effects ◽  
Sum Of Squares ◽  
Forage Yield ◽  
Environment Interaction ◽  
Genotype Environment Interaction ◽  
Ammi Model ◽  
Semi Arid Region ◽  
Semi Arid

Genotype–environment interaction and yield stability were evaluated for 19 genotypes of lovegrass (Eragrostis curvula). The study was conducted in the central semi-arid region of Argentina. Three locations and two growing seasons in combination generated six environments. Genotypic responses and stability of yield under variable environments were investigated. The genotype–environment interaction was analysed by three methods: regression analysis, AMMI and principal coordinates analysis (PCO). Analysis of variance showed that effects of genotype, environment and genotype–environment interaction were highly significant (P < 0·01). The genotypes accounted for 20% of the treatment sum of squares, with environment responsible for 65% and interaction for 14·5%. The biplot indicated that there was partial agreement between the AMMI and regression model. However the scatter point diagrams obtained from PCO analysis revealed only limited agreement with the results obtained by the regression analysis and the AMMI model. The results show that the AMMI model as a whole explained twice as much of the interaction sum of squares as did regression analysis and was more adequate than PCO analysis in quantifying environment and genotype effects for forage yield. AMMI analysis of the genotype–environment interaction effects showed that there were responses characteristic of a particular location. This type of association implies some predictability of genotype–environment interaction effects on forage yield production when differential responses across genotypes are associated with locations. Environmental factors may contribute to the interpretations of genotype–environment interaction. However in the semi-arid region, where fluctuations in growing conditions are unpredictable, additional research is required to obtain an integration of interaction analysis with external environmental (or genotypic) variables.

scholarly journals DESEMPENHO DE CULTIVARES DE SOJA EM DIFERENTES AMBIENTES DE CULTIVO

Nativa ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 390-396
Author(s):  
Paulo Henrique Cerutti ◽  
Marcio Dos Santos ◽  
Anne Tietjen Muniz ◽  
Arthur Ribeiro Rodrigues ◽  
Luan Tiago dos Santos Carbonari ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Grain Yield ◽  
Linear Regression Analysis ◽  
Relevant Information ◽  
Environment Interaction ◽  
Breeding Programs ◽  
Soybean Cultivars ◽  
Genotype Environment Interaction ◽  
Analogous Behavior

Anualmente, inúmeros cultivares de soja são desenvolvidos por programas de melhoramento genético. Desse modo, é importante obter informações sobre o comportamento desses cultivares em distintos ambientes. Objetivou-se com a elaboração do trabalho avaliar o efeito da interação genótipo*ambiente no desempenho de cultivares de soja em diferentes ambientes de cultivo. O delineamento experimental utilizado foi de blocos ao acaso com três repetições. Durante a execução dos experimentos, foi avaliado o desempenho produtivo de seis cultivares de soja em seis ambientes. A variável considerada foi o rendimento de grãos (kg ha-1). As informações foram submetidas a análise de variância, análise de regressão linear simples e teste de comparação de médias. A média geral de produtividade de grãos foi de 2960 kg ha-1. Aanálise de regressão indicou dois cultivares com adaptabilidade ampla, três cultivares com adaptabilidade específica a ambientes desfavoráveis e um cultivar com adaptabilidade específica a ambientes favoráveis. Dentre os cultivares avaliados, quatro apresentaram comportamento esperado ao longo dos ambientes de cultivo. Os cultivares exibiram comportamento análogo quanto ao rendimento de grãos. Por meio da aplicação da metodologia da regressão linear, foi possível obter informações relevantes para cultivo de soja em ambientes subsequentes.Palavras-chave: Glicine max L.; interação genótipo*ambiente; adaptabilidade; estabilidade. PERFORMANCE OF SOYBEAN CULTIVARS IN DIFFERENT GROWING ENVIRONMENTS ABSTRACT:Annually, numerous soybean cultivars are developed by breeding programs. Thus, is important to obtain information about of these cultivars behavior in different environments. The objective of this work was to evaluate the effect of the genotype * environment interaction on the performance of soybean cultivars in different growing environments. The experimental design used was randomized blocks with three replications. During the execution of the experiments, was evaluated the productive performance of six soybean cultivars in six environments. The trait considered was grain yield (kg ha-1). The information was submitted to analysis of variance, simple linear regression analysis and means comparison test. The overall mean grain yield was 2960 kg ha-1. Regression analysis indicated two cultivars with broad adaptability, three cultivars with specific adaptability to unfavorable environments and one cultivar with specific adaptability to favorable environments. Among the evaluated cultivars, four showed prospective behavior throughout the cultivation environments. The cultivars exhibited analogous behavior regarding grain yield. The application of the linear regression methodology provided relevant information for soybean cultivation in subsequent environments.Keywords: Glicine max L.; genotype*environment interaction; adaptability; stability.

scholarly journals Prediction of Hevea progeny performance in the presence of genotype-environment interaction

2009 ◽  
Vol 52 (1) ◽  
pp. 25-33 ◽  
Author(s):  
Paulo de Souza Gonçalves ◽  
Mário Luiz Teixeira de Moraes ◽  
Marcelo de Almeida Silva ◽  
Lígia Regina Lima Gouvêa ◽  
Adriano Tosoni da Eira Aguiar ◽  
...  
Keyword(s):  
Regression Coefficients ◽  
Environment Interaction ◽  
Bark Thickness ◽  
Environmental Index ◽  
Asian Origin ◽  
Genotype X Environment ◽  
General Stability ◽  
Genotype Environment Interaction ◽  
Genetically Improved ◽  
Linear Regressions

Twenty two open-pollinated Hevea progenies from different parental clones of the Asian origin were tested at five sites in the Northwestern São Paulo State Brazil to investigate the progeny girth growth, rubber yield, bark thickness and plant height. Except for the rubber yield, the analysis of variance indicated highly significant (p<0.01) genotype x environment interaction and heterogeneity of regressions among the progenies. However, the regression stability analysis identified only a few interacting progenies which had regression coefficients significantly different from the expected value of one. The linear regressions of the progeny mean performance at each test on an environmental index (mean of all the progenies in each test) showed the general stability and adaptability of most selected Hevea progenies over the test environments. The few progenies which were responsive and high yielding on different test sites could be used to maximize the rubber cultivars productivity and to obtain the best use of the genetically improved stock under different environmental conditions.

scholarly journals Ramularia collo-cygni: a new pathogen spreading in barley fields in Estonia

2018 ◽  
Vol 27 (2) ◽  
Author(s):  
Andres Mäe ◽  
Pille Sooväli ◽  
Lee Põllumaa
Keyword(s):  
Natural Infection ◽  
Spring Barley ◽  
Growth Stages ◽  
Environment Interaction ◽  
Barley Cultivars ◽  
Crop Fields ◽  
Genotype Environment Interaction ◽  
Ramularia Leaf Spot ◽  
Barley Leaves

Ramularia leaf spot (RLS) caused by the fungus Ramularia collo-cygni (Rcc) is affecting barley fields throughout temperate regions worldwide. The first finding of RLS in Estonia was reported on spring barley in 2012 and since then the area of RLS infection has been widening in Estonia. This work has been carried out to monitor the natural infection of Rcc in two winter barley cultivars and to follow artificial fungal infection by a PCR-based assay. Using our approach, we could detect presence of the fungal pathogen in barley leaves before the appearance of disease symptoms at early growth stages. Response of two tested cultivars to Rcc infection in the field conditions was different, showing genotype-environment interaction in the development and spreading of Rcc. In harvested grain samples at the end of growing season no Rcc infection was detected. The role of external inoculum, Rcc conidia transmitted from various grasses (Poaceae) growing next to crop fields, is discussed. These results provide further insight into the epidemiology of Rcc.

scholarly journals ADAPTIBILITY PERFORMANCES OF CHICKPEA (CICER ARIETINUM L.) GENOTYPES UNDER DIFFERENT ENVIRONMENTS FOR STABILITY OF QUANTITATIVE TRAITS

2020 ◽  
Vol 53 (1) ◽  
pp. 62-72
Author(s):  
M. G. AZAM ◽  
M. A. HOSSAIN ◽  
J. HOSSAIN ◽  
M. A. HOSSAIN ◽  
M. O. ALI
Keyword(s):  
Regression Analysis ◽  
Cicer Arietinum ◽  
Block Design ◽  
Principal Component ◽  
High Yield ◽  
Environment Interaction ◽  
Phenotypic Characters ◽  
Cicer Arietinum L ◽  
Genotype Environment Interaction ◽  
Wide Range

The evaluation and computation of yield stability of a genotype over environments is a critical component of a certain breeding program. The present study was intended to screen 11 advance chickpea (Cicer arietinum L.) genotypes and one check for genotype × environment interaction (G × E) at six locations with varying micro and macro climatic conditions for yield correlated phenotypic characters. A number of 11 advanced genotypes of chickpea and one check variety were assessed for their adaptability at six different locations of Bangladesh. The randomized complete block design (RCBD) with three replications was chosen to experiment. The means were used to compute Additive Main Effects and Multiplicative Interaction (AMMI) analysis of variance, followed by regression analysis to measure × E. The regression analysis showed significant genotype × environment interaction for all the phenotypic characters. The mean values of days to flowering, days to maturity, plant height, number of pods per plant and seed yield were highly significant for linear, as well as non-linear components of G × E. Chickpea yield was significantly (p< 0.01) affected by genotypes, the environments and G × E interaction, indicating that the varieties and the test environments were diverse. G × E was further partitioned by principal component axes. The first two principal components cumulatively explained 86.59% of the total variation, of which 53.34% and 33.25% were contributed by IPCA1 and IPCA2, respectively. The AMMI stability value discriminated genotypes G2 (BCX 09010-9), G3 (BCX 09010-2) and G8 (BCX 01008-4) the stable genotypes. The investigated genotypes exhibited varying adaptability in different environments. Genotypes G3 (BCX 09010-9) and G9 (BCX 01008-3) were stable genotypes with high yield over a wide range of environments are promising candidate chickpea varieties.

scholarly journals Conventional Selection versus Methods that Use Genotype × Environment Interaction in Sweet Corn Trials

HortScience ◽  
1992 ◽  
Vol 27 (5) ◽  
pp. 436-438 ◽  
Author(s):  
V.R. Bachireddy ◽  
R. Payne ◽  
K.L. Chin ◽  
M.S. Kang
Keyword(s):  
Sweet Corn ◽  
Yield Reduction ◽  
Environment Interaction ◽  
Selection Methods ◽  
Environmental Index ◽  
Data Set ◽  
Linear Effect ◽  
Genotype Environment Interaction ◽  
Stable Performance ◽  
The Mean

The analysis of variance of a data set made up of 30 sweet corn (Zea mays L.) hybrids evaluated over 5 years for marketable ears (dozens per hectare) indicated a significant genotype (hybrid) × year (GY) interaction. Three selection methods were compared: 1) a conventional method based on mean yield alone (YA), 2) Kang's ranksum (KRS) method, and 3) Kang's modified rank-sum (KMR) method. The number of hybrids selected on the basis of YA, KRS, and KMR was 13. The KRS selected the lowest number of unstable hybrids (three) compared with the YA and KMR, which selected eight and six unstable hybrids, respectively. The mean yields of the selected hybrids were 3034 dozen/ha for YA, 2945 dozen/ha for KRS, and 3019 dozen/ha for KMR. The mean yield of KRS-selected hybrids and KMR-selected hybrids was <2.9% and 0.5%, respectively, than that of YA-based selections. This yield reduction was regarded as insignificant considering the farmer would be able to choose more consistently performing hybrids on the basis of KRS than on the basis of KMR or YA. Heterogeneity due to environmental index is the mean of all genotypes in the jth year and X is the overall mean) was significant and was removed from the GY interaction. The removal of heterogeneity revealed that hybrids 77-2269, 116-Kandy Korn-EH, Golden Queen, 141-Sundance, Merit, and Stowell Evergreen were unstable because of a linear effect of the environmental index, and that hybrids 76-2681 and 806F-Truckers showed stable performance due to a linear effect of the environmental index.

Sign in / Sign up

Export Citation Format

Share Document