A METHOD FOR ASSESSING REGIONAL TRIAL DATA WHEN THE TEST CULTIVARS ARE UNBALANCED WITH RESPECT TO LOCATIONS

1988 ◽  
Vol 68 (4) ◽  
pp. 1103-1110 ◽  
Author(s):  
C. S. LIN ◽  
M. R. BINNS
Keyword(s):  
Selection Criterion ◽  
Trial Data ◽  
P Value ◽  
Parameter Estimates ◽  
Environment Interaction ◽  
Genotype Environment Interaction ◽  
Cooperative Trial ◽  
Cultivar Assessment ◽  
Two Parameters ◽  
Mean Square Distance

A method for assessing regional trial data when test cultivars are not balanced over all locations is proposed. The method is designed primarily for analyzing Eastern cooperative trial data in which the first year’s cultivars are tested only in the subregion (province) where they were developed. Two parameters are estimated for each cultivar: the superiority measure (P), defined as the mean square distance between the cultivar’s response and the maximum response, averaged over the locations where it was tested; and the projected cultivar mean based over all locations on the regression model for genotype × environment interaction. The first parameter indicates how close the response of a cultivar is to the maximum, and the second parameter estimates what the overall performance would have been had cultivars been balanced over all locations. The selection criterion is the P-value, while the projected mean is supplementary; its function is to check if the estimated P-value is realistic. When the rank of the P-value differs considerably from that of the projected mean, investigation of the data by plotting is necessary.Key words: Unbalanced two-way classification data, cultivar assessment, regional trial, superiority measure

PROCEDURAL APPROACH FOR ASSESSING CULTIVAR-LOCATION DATA: PAIRWISE GENOTYPE-ENVIRONMENT INTERACTIONS OF TEST CULTIVARS WITH CHECKS

1985 ◽  
Vol 65 (4) ◽  
pp. 1065-1071 ◽  
Author(s):  
C. S. LIN ◽  
M. R. BINNS
Keyword(s):  
Simple Procedure ◽  
Trial Data ◽  
Assessment Process ◽  
Environment Interaction ◽  
Cluster Analyses ◽  
Location Data ◽  
Procedural Approach ◽  
Genotype Environment Interaction ◽  
Complementary Nature ◽  
Cultivar Assessment

A simple procedure for assessing cultivar-location data is proposed. The procedure consists of comparisons between checks and each of the test cultivars to find those test cultivars for which comparison with the checks can be done ignoring variability among locations, and to isolate those for which a location-by-location comparison is advisable. Since the level at which a comparison-wise genotype-environment interaction is deemed to be important can be set by the user, this represents a great simplification of the assessment process. A set of regional trial data was used as an example to demonstrate the approach. The complementary nature of this method with cluster analyses which group cultivars or locations is discussed.Key words: Genotype-environment interaction, cultivar assessment, barley yield

Identification of new stable and high iron rich fertility restorers in pearl millet

2019 ◽  
Vol 79 (3) ◽  
Author(s):  
Tripti . Singhal ◽  
S. P. Singh ◽  
S. Mukesh Sankar ◽  
C. . Bharadwaj ◽  
C. . Bharadwaj ◽  
...  
Keyword(s):  
Pearl Millet ◽  
Pennisetum Glaucum ◽  
Fertility Restoration ◽  
P Value ◽  
Environment Interaction ◽  
Nutritional Security ◽  
High Iron ◽  
Genotype Environment Interaction ◽  
Promising Line ◽  
Fertility Restorers

Biofortification of pearl millet (Pennisetum glaucum (L.) R. Br.) with improved iron (Fe) and zinc (Zn) will have great impact as it is an indispensable component of nutritional security of inhabitants of arid and semi-arid regions. Ten genotypes along with checks were evaluated in RBD in six locations during kharif, 2016 under rainfed conditions. Significant differences were observed in genotype, environment and genotype × environment interaction mean squares for grain Fe and Zn contents, indicating differential nutrient accumulation by the genotypes. The first two principal components obtained in AMMI analysis were significant and cumulatively explained the total variation were 81.47 % for Fe and 73.97 % for Zn. A positive and moderately high correlation (r=0.6) between Fe and Zn contents suggests good prospects of simultaneous improvement for both micronutrients. Among the ten genotypes, PPMI 953 was found to be more stable with high mean Fe (90 ppm) and Zn (59 ppm) contents. On crossing with designated A lines of pearl millet, the line PPMI 953 found to be restorer for A1 system with complete fertility restoration of F1 panicle of the cross, ICMA(1) 863 x PPMI 953 under bagged condition and resulting F1 with 78-84% fertility measured by seed setting % under bag. The F2 individuals showed 9:7 fertility-sterility ratio (χ 2 value=0.002, P value=0.964). The promising line, PPMI 953 may be used as source for further genetic improvement with respect to grain micronutrient content or can be directly used as male parent in development of high iron pearl millet hybrids.

Analysis of Indian post-rainy sorghum multi-location trial data reveals complexity of genotype × environment interaction

2016 ◽  
Vol 155 (1) ◽  
pp. 44-59 ◽  
Author(s):  
S. RAKSHIT ◽  
K. N. GANAPATHY ◽  
S. S. GOMASHE ◽  
A. DHANDAPANI ◽  
M. SWAPNA ◽  
...  
Keyword(s):  
Trial Data ◽  
Conservation Practices ◽  
Environment Interaction ◽  
Improvement Project ◽  
Genotype Environment Interaction ◽  
Main Effect ◽  
Highly Correlated ◽  
Main Effects ◽  
Major Emphasis ◽  
New Cultivars

SUMMARYSorghum [Sorghum bicolor (L.) Moench] grown in India is of two adaptive types: rainy and post-rainy. The post-rainy sorghum is predominantly consumed by humans. While releasing new cultivars through multi-location testing, major emphasis is given to the superiority of new cultivars over existing cultivars, with very little emphasis on the genotype × environment interaction (GEI). To understand the complexity of GEI in post-rainy sorghum testing location trials, the multi-location evaluation data of two post-rainy seasons (2009/10 and 2010/11) under the All India Coordinated Sorghum Improvement Project were analysed. In both years, location explained the highest proportion of total sum of squares followed by the GEI effect and main effect of genotype. Additive main effects and multiplicative interaction (AMMI), stability values (ASV) and genotype + genotype × environment interaction (GGE) instability values recorded high correlation resulting in identification of the best performing cultivars. However, the rank correlations were lower, though still significant. A mixture of crossover and non-crossover GEI was a common occurrence in both years. ‘Which-won-where’ analysis suggested the existence of four possible mega-environments (ME) among post-rainy testing locations, with a few non-informative locations within ME. Mega-environments are characterized by soil type, rainfall pattern and moisture conservation practices. The present study indicated the possibility of reducing the number of test locations by eliminating non-representative highly correlated locations and suggested the need to breed for location-specific genotypes rather than genotypes with wider adaptability.

GENOTYPE × ENVIRONMENT INTERACTIONS FOR EAR MOLD RESISTANCE AND ITS SUBCOMPONENTS IN MAIZE HYBRIDS

1986 ◽  
Vol 66 (2) ◽  
pp. 291-297 ◽  
Author(s):  
R. B. HUNTER ◽  
J. F. MULDOON ◽  
G. N. ATLIN
Keyword(s):  
Recurrent Selection ◽  
Zea Mays L ◽  
Selection Criterion ◽  
Gibberella Zeae ◽  
Environment Interaction ◽  
Weather Variables ◽  
Maize Hybrids ◽  
Genotype Environment Interaction ◽  
Mold Resistance ◽  
Spread Of Infection

Twelve short season maize (Zea mays L.) hybrids were evaluated over five environments between 1979 and 1982 to determine the magnitude of the genotype × environment interaction for ear mold (Gibberella zeae (Schwabe)) resistance. Mold damage (MD) was characterized into two subcomponents, percent infected plants (%IP) and spread of infection on infected ears (SI). All three traits (%MD, %IP and SI) exhibited highly significant genotype × environment interactions. Weather variables were considered to account for an insignificant proportion of this variation due to the nature of the artificial inoculaton procedure. An undetermined proportion of the variation was likely attributable to differences in fungal isolates used over the years. Estimates of stability indicated that four of the 12 hybrids had relatively low mold damage and high stability over the five environments. Broad-sense heritabilities estimated over the five environments were 0.73, 0.84 and 0.54 for %MD, %IP and SI, respectively. Evaluation of mold damage in one environment gave estimated heritabilities of 0.35, 0.50 and 0.19 for the three traits, respectively. Low genotypic correlation between %IP and SI indicated that they are in fact substantially different traits which may be selected for independently. Percent infected plants was a more stable trait than percent mold damage per se when selection occurred in one environment and, depending on the relative proportions of additive and nonadditive gene action conditioning the two traits, %IP may be a more suitable selection criterion in a recurrent selection program than percent mold damage.Key words: Gibberella zea, initial infection, spread of infection, corn, maize, ear mold

A SUPERIORITY MEASURE OF CULTIVAR PERFORMANCE FOR CULTIVAR × LOCATION DATA

1988 ◽  
Vol 68 (1) ◽  
pp. 193-198 ◽  
Author(s):  
C. S. LIN ◽  
M. R. BINNS
Keyword(s):  
Maximum Response ◽  
Environment Interaction ◽  
Mean Square ◽  
Screening Process ◽  
Location Data ◽  
Genotype Environment Interaction ◽  
The Mean ◽  
The Difference ◽  
Cultivar Assessment ◽  
Cultivar Responses

A measure of cultivar general superiority for cultivar × location data is defined as the distance mean square between the cultivar’s response and the maximum response averaged over all locations. Since the maximum response is the upper boundary in each location, a small mean square indicates general superiority of the test cultivar. The method requires that all test cultivars be balanced over locations but not necessarily the checks. The advantage of the proposed method are: (i) The checks provide only a plausible maximum response for each location and are not required for assessing the test cultivars. Thus checks do not have to be present in all locations. This allows a greater flexibility for a breeder to choose locally adapted cultivars as checks without unduly increasing the size of a regional trial. (ii) The measure of general superiority consists of only one parameter, thus simplifying the screening process considerably. A subsidiary parameter for interaction can be used to indicate lack of general adaptability. (iii) The difference between the mean of the maximum response averaged over all locations and the mean of the best cultivar provides useful information as to how many cultivars are needed to achieve optimum productivity for the entire region, (iv) The specific adaptability of a cultivar can be identified by plotting the maximum and the test cultivar responses on the location means.Key words: Genotype × environment interaction, cultivar assessment, regional trial

Genotype-Environment Interaction in Calf Production. II. Live Measurements and Carcass Traits

1973 ◽  
Vol 36 (3) ◽  
pp. 471-475 ◽  
Author(s):  
T. R. Batra ◽  
W. R. Usborne ◽  
D. G. Grieve ◽  
E. B. Burnside
Keyword(s):  
Carcass Traits ◽  
Environment Interaction ◽  
Genotype Environment Interaction

METHODS FOR EVALUATING THE GENOTYPE-ENVIRONMENT INTERACTION IN BREEDING WINTER WHEAT FOR PRODUCTIVITY

2020 ◽  
Vol 15 (1) ◽  
pp. 56-64
Author(s):  
Irina Manukyan ◽  
◽  
Madina Basieva ◽  
Elena Miroshnikova ◽  
◽  
...  
Keyword(s):  
Winter Wheat ◽  
Environment Interaction ◽  
Genotype Environment Interaction
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