Optimizing seedling screening method for anthracnose resistance in watermelon

2021 ◽  
Author(s):  
Subas Malla ◽  
Kevin M Crosby ◽  
Edgar Correa
Keyword(s):  
Screening Method ◽  
Post Inoculation ◽  
Large Set ◽  
Gge Biplot ◽  
Seedling Resistance ◽  
Inoculum Concentration ◽  
Anthracnose Resistance ◽  
Genotype By Environment ◽  
Significant Difference ◽  
Race 2

Among three races of Colletotrichum orbiculare, causes of anthracnose of cucurbits, screening for race 2 resistance was studied under greenhouse conditions at various inoculum concentrations and rated plants on different days post inoculation (DPI). The objectives of this study were optimizing inoculum concentration and phenotyping DPI for seedling resistance. Five inoculum concentrations were compared (2.5 x 104, 5 x 104, 1 x 105, 2.5 x 105, and 5 x 105 conidial spore ml-1). Four watermelon genotypes, ‘Black Diamond’, ‘Charleston Gray 133’, PI 543210, PI 189225, and two cucumber genotypes, ‘Marketer’, and ‘H19 Little Leaf’ were evaluated. Disease was recorded on the percentage of cotyledon area lesion (PCL), severity of hypocotyl lesion (SHL), severity of petiole lesion (SPL), percentage of leaf area lesion (PLL), as well as a disease index (INDX) from 5 to 14 DPI. There was a significant difference among genotypes and inoculum concentrations. The resistant PI 189225 was significantly different (P < 0.05) from the highly susceptible PI 543210. Inoculum 1 x 105 spore ml-1 was at par with 5 x 105 and 2.5 x 105 but significantly different from 5 x 104 and 2.5 x 104 for AUDPS PLL, AUDPS INDX, AUDPS SPL, and AUDPS SHL. Inoculum at 1 x 105 spore ml-1 was the most optimal to differentiate germplasm. Genotype plus genotype-by-environment (GGE) biplot showed that PLL was a representative trait. A single PLL rating on 9 DPI would optimize resources for screening a large set of germplasm for anthracnose resistance in a watermelon breeding program.

Efficacy of three greenhouse screening methods for the identification of physiological resistance to white mold in dry bean

2009 ◽  
Vol 89 (4) ◽  
pp. 755-762 ◽  
Author(s):  
H Terán ◽  
S P Singh
Keyword(s):  
Phaseolus Vulgaris ◽  
Common Bean ◽  
Sclerotinia Sclerotiorum ◽  
Screening Method ◽  
White Mold ◽  
Screening Methods ◽  
Post Inoculation ◽  
Dry Bean ◽  
Physiological Resistance ◽  
Nueva Granada

White mold (WM) caused by Sclerotinia sclerotiorum (Lib.) de Bary is the most devastating disease of common bean (dry and snap or garden bean) (Phaseolus vulgaris L.) in North America. The use of a reliable screening method (SM) in common bean is crucial to improve physiological resistance to WM. The objective of this study was to compare the efficacy of three SM to identify physiological resistance in dry bean genotypes with different evolutionary origins and levels of resistance. Screening methods tested were: (i) the modified straw test or cut–stem (CSM); (ii) infected bean flower (IFL); and (iii) infected oat seed (IOS). A 195, ICA Bunsi, Othello, and VCW 54 dry bean were tested with the three SM. The experimental design was a split plot in randomized complete blocks with three replications in 2007 and 2008. Two independent inoculations 1 wk apart for each SM were made. The WM reaction was scored at 16, 23, and 33 d post-inoculation (DPI) using a 1 to 9 scale. There were highly significant differences between SM and its interaction with years. The CSM and IFL were the most consistent and highly correlated (r > 0.70, P < 0.01). Interspecific breeding line VCW 54 consistently had the highest WM resistance across years, SM, and evaluation dates, followed by A 195. White mold scores increased with delayed evaluations. Thus, CSM or IFL with disease assessed 33 DPI should be used for identifying common bean genotypes with high levels of physiological resistance to WM.Key words: Common bean, growth habit, race Mesoamerica, race Nueva Granada, Phaseolus vulgaris, Sclerotinia sclerotiorum

Delineation of genotype-by-environment interactions for identification and validation of resistant genotypes in chickpea to fusarium wilt using GGE biplot

2021 ◽  
Vol 144 ◽  
pp. 105571
Author(s):  
A.K. Srivastava ◽  
D.R. Saxena ◽  
P.R. Saabale ◽  
K.S. Raghuvanshi ◽  
V.P. Anandani ◽  
...  
Keyword(s):  
Fusarium Wilt ◽  
Gge Biplot ◽  
Genotype By Environment Interactions ◽  
Genotype By Environment ◽  
Resistant Genotypes

scholarly journals IDENTICAL WORD MEANING “INTERPRETATION AND TRANSLATION” IN THE CONTEXT OF CORPUS-BASED SENTENCES

2020 ◽  
Vol 2 (3) ◽  
pp. 189-195
Author(s):  
Mega Febriani Sya ◽  
Ninuk Lustyantie ◽  
Miftahulkhairah Anwar
Keyword(s):  
Speech Acts ◽  
First Language ◽  
Word Meaning ◽  
Occurrence Rate ◽  
Large Set ◽  
Frequency Of Occurrence ◽  
Significant Difference ◽  
Meaning Interpretation ◽  
Authentic Data ◽  
Data Collection Procedure

For students whose first language is not a native English speaker, at first glance they will interpret the words "translation" and "interpretation" with the same meaning. Both are indeed similar and both function to transfer one language to another. This study aims to identify the frequency of occurrence of the words "translation" and "interpretation" in several situations in different speech contexts. The method used in this study is the corpus method, to see a large set of authentic data which provides clearer information about the frequency of occurrence of the words "translation" and "interpretation" in several actual contexts of different speech acts. The data collection procedure on the corpus and its analysis uses the facilities provided by the corpus page, namely "chart". The results show that the comparison of the frequency of occurrence of the words "translation" and "interpretation" in the context of different speech shows a significant difference, the word interpretation has a higher occurrence rate of 4282 times than translation, which appeared 1405 times.   The meaning of this finding is that the word interpretation is more widely used in the context of formal academic sentences because interpretation does not only have a meaning for itself but can also function as a continuation of meaning from the translation.  

scholarly journals Rice cultivar selection in an agroforestry system through GGE-biplot and EBLUP

2021 ◽  
Vol 22 (11) ◽  
Author(s):  
Taufan Alam ◽  
Priyono Suryanto ◽  
Supriyanta Supriyanta ◽  
Panjisakti Basunanda ◽  
Rani Agustina Wulandari ◽  
...  
Keyword(s):  
Rice Cultivar ◽  
Agroforestry System ◽  
Agroforestry Systems ◽  
Soil Types ◽  
Environment Interaction ◽  
Rice Cultivars ◽  
Gge Biplot ◽  
Genotype By Environment ◽  
Cultivar Selection ◽  
Melaleuca Cajuputi

Abstract. Alam T, Suryanto P, Supriyanta, Basunanda P, Wulandari RA, Kastono D, Widyawan MH, Nurmansyah, Taryono. 2021. Rice cultivar selection in an agroforestry system through GGE-biplot and EBLUP. Biodiversitas 22: 4750-4757. Genotype-by-environment interaction (GEI) causes differences in the productivity of rice cultivars in agroforestry systems. For this reason, the stability of rice cultivars is an important aspect that should be considered before a cultivar is recommended to farmers. Superior genotypes and ideal environments are commonly identified using two statistical models, namely, genotype–genotype-by-environment biplot (GGE-biplot) and empirical best linear unbiased prediction (EBLUP). In this study, 15 rice cultivars were evaluated in terms of their productivity and stability in three soil types (Lithic Haplusterts, Ustic Epiaquerts, and Vertic Haplustalfs) in an agroforestry system with kayu putih (Melaleuca cajuputi) in 2019 and 2020 at the Menggoran Forest Resort, Playen Forest Section, Yogyakarta Forest Management District, Indonesia. The cultivars were treated as random effects to select and obtain the EBLUP of the best cultivars in each soil type. The EBLUP revealed that Situ Patenggang showed the highest yields of 4.887 and 5.456 tons ha?1 in Lithic Haplusterts and Vertic Haplustalfs, respectively. GM 28 exhibited the highest yield of 6.492 tons ha?1 in Ustic Epiaquerts. Ciherang, GM 2, GM 8, GM 11, GM 28, Inpari 6 Jete, Inpari 33, IR-64, and Way Apo Buru were classified as stable and fairly stable cultivars, whereas the other cultivars were unstable. Therefore, rice cultivars with high yields in specific soil types should be selected.

Evaluation of sugarcane genotypes with respect to sucrose yield across three crop cycles using GGE biplot analysis

2021 ◽  
pp. 1-13
Author(s):  
Aliya Momotaz ◽  
Per H. McCord ◽  
R. Wayne Davidson ◽  
Duli Zhao ◽  
Miguel Baltazar ◽  
...  
Keyword(s):  
Block Design ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Genotype By Environment ◽  
Biplot Analysis ◽  
Genotype Environment Interaction ◽  
Area 5 ◽  
Randomized Complete Block Design ◽  
Main Effects

Summary The experiment was carried out in three crop cycles as plant cane, first ratoon, and second ratoon at five locations on Florida muck soils (histosols) to evaluate the genotypes, test locations, and identify the superior and stable sugarcane genotypes. There were 13 sugarcane genotypes along with three commercial cultivars as checks included in this study. Five locations were considered as environments to analyze genotype-by-environment interaction (GEI) in 13 genotypes in three crop cycles. The sugarcane genotypes were planted in a randomized complete block design with six replications at each location. Performance was measured by the traits of sucrose yield tons per hectare (SY) and commercial recoverable sugar (CRS) in kilograms of sugar per ton of cane. The data were subjected to genotype main effects and genotype × environment interaction (GGE) analyses. The results showed significant effects for genotype (G), locations (E), and G × E (genotype × environment interaction) with respect to both traits. The GGE biplot analysis showed that the sugarcane genotype CP 12-1417 was high yielding and stable in terms of sucrose yield. The most discriminating and non-representative locations were Knight Farm (KN) for both SY and CRS. For sucrose yield only, the most discriminating and non-representative locations were Knight Farm (KN), Duda and Sons, Inc. USSC, Area 5 (A5), and Okeelanta (OK).

scholarly journals GENOTYPE BY ENVIRONMENT INTERACTION IN COWPEA LINES USING GGE BIPLOT METHOD

2018 ◽  
Vol 31 (1) ◽  
pp. 64-71 ◽  
Author(s):  
MASSAINE BANDEIRA E SOUSA ◽  
KAESEL JACKSON DAMASCENO-SILVA ◽  
MAURISRAEL DE MOURA ROCHA ◽  
JOSÉ ÂNGELO NOGUEIRA DE MENEZES JÚNIOR ◽  
LAÍZE RAPHAELLE LEMOS LIMA
Keyword(s):  
Maximum Yield ◽  
Genotype By Environment Interaction ◽  
Superior Performance ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Brazilian Cerrado ◽  
Genotype By Environment ◽  
Biplot Analysis ◽  
Adjusted Means ◽  
Selection Of

ABSTRACT The GGE Biplot method is efficien to identify favorable genotypes and ideal environments for evaluation. Therefore, the objective of this work was to evaluate the genotype by environment interaction (G×E) and select elite lines of cowpea from genotypes, which are part of the cultivation and use value tests of the Embrapa Meio-Norte Breeding Program, for regions of the Brazilian Cerrado, by the GGE-Biplot method. The grain yield of 40 cowpea genotypes, 30 lines and 10 cultivars, was evaluated during three years (2010, 2011 and 2012) in three locations: Balsas (BAL), São Raimundo das Mangabeiras (SRM) and Primavera do Leste (PRL). The data were subjected to analysis of variance, and adjusted means were obtained to perform the GGE-Biplot analysis. The graphic results showed variation in the performance of the genotypes in the locations evaluated over the years. The performance of the lines MNC02-675F-4-9 and MNC02-675F-4-10 were considered ideal, with maximum yield and good stability in the locations evaluated. There mega-environments were formed, encompassing environments correlated positively. The lines MNC02-675F-4-9, MNC02-675F-9-3 and MNC02-701F-2 had the best performance within each mega-environment. The environment PRL10 and lines near this environment, such as MNC02-677F-2, MNC02-677F-5 and the control cultivar (BRS-Marataoã) could be classified as those of greater reliability, determined basically by the genotypic effects, with reduced G×E. Most of the environments evaluated were ideal for evaluation of G×E, since the genotypes were well discriminated on them. Therefore, the selection of genotypes with adaptability and superior performance for specific environments through the GGE-Biplot analysis was possible.

scholarly journals Adaptability and Stability Study of Selected Sweet Sorghum Genotypes for Ethanol Production under Different Environments Using AMMI Analysis and GGE Biplots

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Justice Kipkorir Rono ◽  
Erick Kimutai Cheruiyot ◽  
Jacktone Odongo Othira ◽  
Virginia Wanjiku Njuguna ◽  
Joseph Kinyoro Macharia ◽  
...  
Keyword(s):  
Sweet Sorghum ◽  
Ethanol Yield ◽  
Block Design ◽  
Cane Yield ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Genotype By Environment ◽  
Cane Juice ◽  
Juice Yield ◽  
Sorghum Genotypes

The genotype and environment interaction influences the selection criteria of sorghum (Sorghum bicolor) genotypes. Eight sweet sorghum genotypes were evaluated at five different locations in two growing seasons of 2014. The aim was to determine the interaction between genotype and environment on cane, juice, and ethanol yield and to identify best genotypes for bioethanol production in Kenya. The experiments were conducted in a randomized complete block design replicated three times. Sorghum canes were harvested at hard dough stage of grain development and passed through rollers to obtain juice that was then fermented to obtain ethanol. Cane, juice, and ethanol yield was analyzed using the additive main effect and multiplication interaction model (AMMI) and genotype plus genotype by environment (GGE) biplot. The combined analysis of variance of cane and juice yield of sorghum genotypes showed that sweet sorghum genotypes were significantly (P<0.05) affected by environments (E), genotypes (G) and genotype by environment interaction (GEI). GGE biplot showed high yielding genotypes EUSS10, ACFC003/12, SS14, and EUSS11 for cane yield; EUSS10, EUSS11, and SS14 for juice yield; and EUSS10, SS04, SS14, and ACFC003/12 for ethanol yield. Genotype SS14 showed high general adaptability for cane, juice, and ethanol yield.

Visual Immunoassay for Detection of Salmonella in Foods: Collaborative Study

1988 ◽  
Vol 71 (5) ◽  
pp. 973-980 ◽  
Author(s):  
Russell S Flowers ◽  
Mary Joan Klatt ◽  
Susan L Keelan
Keyword(s):  
Enzyme Immunoassay ◽  
Collaborative Study ◽  
Screening Method ◽  
Food Group ◽  
Black Pepper ◽  
Soy Flour ◽  
Significant Difference ◽  
Dry Milk ◽  
Standard Culture ◽  
Culture Procedure

Abstract A collaborative study was performed in 13 laboratories to validate a visual enzyme immunoassay (EIA) procedure, TECRA®, for rapid detection of Salmonella in foods. The EIA method was compared with the standard culture procedure for detection of Salmonella in 6 food types: ground black pepper, soy flour, dried whole eggs, milk chocolate, nonfat dry milk, and raw deboned turkey. Uninoculated and inoculated samples were included in each food group analyzed. There was no significant difference in the productivity of the EIA and culture procedures at the 5% level for any of the 6 foods. The enzyme immunoassay screening method has been approved interim official first action.

scholarly journals Clinical, Ultrasonographic, Bacteriological, Cytological and Histopathological Findings of Uterine Involution in Ewes with Uterine Infection

Pathogens ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 54 ◽  
Author(s):  
Katerina S. Ioannidi ◽  
Natalia G. C. Vasileiou ◽  
Marianna S. Barbagianni ◽  
Denise C. Orfanou ◽  
George Mantziaras ◽  
...  
Keyword(s):  
Reproductive Performance ◽  
Post Partum ◽  
Vaginal Swab ◽  
Post Inoculation ◽  
Tissue Samples ◽  
E Coli ◽  
Uterine Infection ◽  
Uterine Involution ◽  
Group I ◽  
Significant Difference

The objectives of the study were (a) to study the characteristics of uterine involution in ewes that had developed subclinical uterine infection in the immediately post-partum period and (b) to evaluate effects of the infection in the subsequent reproductive performance of ewes. Uterine infection was induced in ewes (I, n = 10) by intrauterine inoculation of Escherichia coli; uninoculated controls were included (C, n = 12). Animals were examined at regular intervals before and post-inoculation. Clinical and ultrasonographic examinations were performed. Vaginal swab samples and biopsy uterine tissue samples were collected for bacteriological, cytological and histological examination. Finally, ewes were put to rams and reproductive performance was monitored. After challenge, it was ultrasonographically found that caruncular dimensions, myometrial thickness and diameter of uterine lumen were greater in I ewes. In these ewes, particular reduction of dimensions occurred during the second week post-partum, whilst in C ewes during the first week. The uterine artery diameter and the blood flow into the uterus were also greater in I than in C ewes. E. coli infection was more frequent and of longer duration in I than in C ewes: in 68.1% and 50.0% of ewes and 19.5 and 14 days, respectively. There was lower proportion of neutrophils and higher of lymphocytes in group I than in C. In inoculated ewes, there was histological evidence of uterine epithelial destruction, increased cellular infiltration, hyperaemia and extracasation, which persisted up to 42 days post-partum. During the subsequent reproductive season, all ewes in group I lambed normally and produced healthy and viable lambs. No significant difference in reproductive performance parameters were seen in I comparison to C ewes. It is concluded that the innate immunity of the uterus sufficed to counteract the bacterial infection, although the process of involution took longer than in healthy animals; moreover, the ultrasonographic examination is a useful means for assessment of the genital tract of ewes post-partum; finally, no adverse effects were noted in the subsequent reproductive performance of ewes.

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