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Plant Breeding - Science topic

Plant breeding, Plant breeders, Crops, Crop Improvement, Genetics
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What is the importance of path analysis in plant breeding ?
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  1. Understanding Complex Traits: It helps untangle the relationships between multiple traits and their direct and indirect effects on outcomes like yield.
  2. Selection of Breeding Traits: Identifies key traits that significantly impact desired outcomes, guiding breeders in prioritizing traits for selection.
  3. Optimizing Breeding Strategies: Informs which parental lines to cross based on trait associations, improving breeding efficiency.
  4. Improving Genetic Gain: Allows for targeted selection, leading to faster genetic advancements in breeding programs.
  5. Evaluating Environmental Effects: Assesses how environmental factors influence trait relationships, aiding in the selection of resilient traits.
  6. Facilitating Decision Making: Provides data-driven insights for more informed breeding decisions.
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In plant breeding research, line x tester mating designs are often used to assess gene action for various traits. Some studies suggest that the simultaneous significant mean squares for lines, testers, and their interaction (lines x testers) indicate the involvement of both additive and non-additive gene action. Conversely, others propose that Baker's ratio, with a value less than one, signifies non-additive gene effects.
Can someone clarify the relative strengths and limitations of these two approaches (mean squares vs. Baker's ratio) in determining the type of gene action governing a trait?
Are there situations where one approach might be more informative than the other?
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Dear Kidanemariam,
Thank you very much for the detailed explanation! It was very helpful in clarifying the strengths and limitations of mean squares vs. Baker's ratio.
Would you be able to recommend any supporting documents or resources that delve deeper into this topic?
Thanks again for your time and expertise.
Best regards,
Adane
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State the reasons in the case of  non crop plants, which is a timber yielding one?
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Dioecy, the condition in which individual plants within a species are either male or female, can pose challenges for breeding and cultivation in certain plants. Here's how dioecy can limit breeding and cultivation:
1. Pollination Limitations: Dioecious plants require pollen from male plants to fertilize the flowers of female plants for seed production. This reliance on separate male and female individuals complicates the pollination process compared to monoecious or hermaphroditic plants, where both male and female reproductive organs are present in the same flower. Pollination efficiency may be reduced if male and female plants are not sufficiently close to facilitate natural pollination, requiring additional efforts for artificial pollination or the introduction of pollinators.
2. Seed Production Challenges: Breeding programs for dioecious plants often require the maintenance of separate male and female breeding lines to ensure controlled pollination and seed production. This segregation of breeding lines can increase the complexity and cost of breeding programs, as it necessitates the maintenance of larger populations and careful management to prevent unintended cross-pollination between lines. Additionally, seed production may be limited if there are insufficient numbers of male or female plants available for breeding purposes.
3. Genetic Variation: Dioecious plants may exhibit sex-linked genetic traits, where certain characteristics are linked to the plant's sex chromosomes. This can complicate breeding efforts, as desired traits may be associated with one sex and not easily transferred to the opposite sex. Limited genetic variation within breeding populations can also restrict the ability to select for desirable traits, potentially leading to reduced crop diversity and resilience to environmental stressors.
4. Propagation Challenges: Propagation of dioecious plants through vegetative means, such as cuttings or grafting, may be limited if the plants exhibit sexual dimorphism, where male and female plants have distinct growth habits or characteristics. This can affect the uniformity and performance of propagated plants, particularly in horticultural or commercial cultivation settings where consistency in plant characteristics is desirable.
5. Cultural Preferences and Market Demand: In some cases, cultural preferences or market demand may favor certain sexes of dioecious plants over others. For example, female plants of certain fruit or ornamental species may be preferred for their fruit production or aesthetic qualities, leading to imbalances in cultivation efforts and potentially limiting the availability of desired plant material.
While dioecy presents challenges for breeding and cultivation, it also offers opportunities for genetic studies, specialized breeding programs, and the development of unique plant varieties. Effective management strategies, such as careful selection of breeding lines, controlled pollination techniques, and integration of dioecious plants into diverse cropping systems, can help mitigate the limitations associated with dioecy and support successful cultivation efforts.
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Hybridization is a technique used in plant breeding to create new plant varieties by crossing two genetically different parent plants. This process involves transferring pollen from the male reproductive organs of one plant, called the pollen donor or male parent, to the female reproductive organs of another plant, called the female parent. The resulting hybrid plants inherit desirable traits from both parents, such as improved yield, disease resistance, or enhanced quality.
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It's a treatment the farmer gives plants to harvest one or more plants. a brand-new plant having unique, appealing qualities in terms of size, color, form, and timing of blooming. Maturity, flavor, aroma, and pest and disease resistance, among other unique qualities. about plants, so that they can adjust to your living circumstances and the local environment
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R Studio, Plant Breeding, Agriculture Statistics.
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R and RStudio are robust tools for data analysis. You can learn various classical genetic analyses tutorials (including variability, path analysis, diversity, cluster analysis, stability, PCA, AMMI and GGE analyses) just from Youtube.
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I am a MS thesis student in Plant Breeding and Biotechnology Labratory, University of Dhaka.
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Just nucleotide complementary rule - Chargaff's rule.
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in terms of abroad and in India
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Plant Pathology has more job opportunities. Plant Pathologist involved in chemical and biological means of plant disease management in the concern industries. In varietal development, screening of germplasm against pathogens or identification of disease resistant genes, Pathologist role is important.
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In plant breeding, what are uses discrimination function.
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Discriminant function technique involves the development of selection criteria on a combination of various characters and aids the breeder in indirect selection for genetic improvement in yield. In plant breeding, the selection index refers to a linear combination of characters associated with yield.
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How can genomic selection be used to improve the efficiency of plant breeding programs?
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Genomic selection can be used to improve the efficiency of plant breeding programs in several ways:
1. Early selection: Genomic selection allows breeders to select plants at an early stage, even before they have fully developed. This saves time and resources by eliminating the need for growing plants to maturity before making selection decisions.
2. Increased accuracy: Genomic selection uses genetic markers to predict the performance of plants for specific traits. This prediction is more accurate than traditional phenotypic selection, which relies on observing the actual expression of traits. By using genomic information, breeders can make more informed decisions about which plants to select for further breeding.
3. Selection for complex traits: Genomic selection is particularly useful for selecting complex traits that are controlled by multiple genes and influenced by environmental factors. Traditional breeding methods often struggle with such traits due to their complexity, but genomic selection can identify markers associated with these traits and enable breeders to select plants with desired combinations of genes.
4. Accelerated breeding cycles: By using genomic selection, breeders can reduce the time required for each breeding cycle. They can quickly identify and select plants with desirable traits, leading to faster generation turnover and more rapid progress in developing improved varieties.
5. Enhanced genetic diversity: Genomic selection allows breeders to access a wider range of genetic diversity by identifying desirable alleles from diverse germplasm sources. This helps in broadening the genetic base of cultivated varieties and reducing the risk of genetic erosion.
6. Cost-effective trait evaluation: Traditional phenotypic evaluation requires extensive field trials and labor-intensive data collection processes. In contrast, genomic selection enables cost-effective trait evaluation through marker-assisted prediction models that require fewer resources and time.
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In the estimation of genetic parameters such as heritability, degree of dominance etc.
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The behaviour or mode of expression of genes in a genetic population is called gene action. Gene action is of two types, viz. additive gene action and non-additive gene action. It is used in the estimation of genetic parameters such as heritability, degree of dominance, selection of breeding methodology etc.
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Pre breeding is essential for linking genetic diversity arising from wild relatives and other unimproved materials to utilization in crop improvement. It is the main link between the germplasm conservation and its use in plant breeding for developing new varieties.
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Pre-breeding aims to isolate desired genetic traits (e.g. disease resistance) from unadapted material like CWR and introduce them into breeding lines that are more readily crossable with modern, elite varieties. Pre-breeding broadens the elite genepool by re-capturing lost beneficial genetic diversity. Pre-breeding provides a unique opportunity, through the introgression of desirable genes from wild germplasm into genetic backgrounds readily used by the breeders with minimum linkage drag, to overcome this.
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Genotype x environment interactions refer to the phenomenon where the performance or expression of a plant's traits is influenced by the specific environmental conditions in which it is grown. This interaction is crucial in plant breeding as different genotypes may exhibit different responses to various environmental factors such as temperature, soil type, moisture availability, and disease pressure. Breeders need to evaluate and select plant varieties that perform consistently well across a range of e​n​v​i​r​o​n​ment.​
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@ Pooran, actually it refers to the differential performance of genotypes in different environments that affect the efficiency of selection in a breeding program. G × E interaction arises due to the differences in the sensitivities of genotypes to the different environmental conditions. It is important for understanding the genetic basis of environmental adaptation.
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What are the differences between genetically modified (GM) and non-GM seeds in terms of seed quality?
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GMO foods are healthful and safe to eat as their non-GMO counterparts. Some GMO plants have actually been modified to improve their nutritional value. An example is GMO soybeans with healthier oils that can be used to replace oils that contain trans fats. GMO crops are used to make ingredients that Americans eat such as cornstarch, corn oil, soybean oil, canola oil, or granulated sugar.
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Molecular markers are specific DNA sequences that can be used to identify and track particular genes or genetic traits of interest in plant breeding programs. These markers serve as tools to assist breeders in selecting plants with desired traits more efficiently and accurately. Molecular markers can be used to determine genetic relatedness, assess genetic diversity within populations, identify specific genes responsible for traits, and facilitate marker-assisted selection (MAS) or marker-assisted breeding (MAB) techniques.
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Molecular markers are commonly used to assess genetic variation in agronomic germplasm, analyze population structure, localize quantitative traits (QTL), or linkage mapping for gene mapping. The most interesting application of molecular markers is marker-assisted selection (MAS). Molecular marker technology enables plant breeders to select individual plants based on their marker pattern (genotype) rather than their observable traits (phenotype). This process is called marker assisted selection (MAS) or marker assisted breeding (MAB). Compared with traditional breeding programs, molecular markers can increase the efficiency and effectiveness of breeding programs. Molecular markers in the construction of linkage maps, they have numerous applications in plant breeding such as assessing the genetic variations within cultivars and germplasms. Molecular markers are one of the most powerful tools for studying genetic diversity. They are used in the study of phylogenetic relationships, selection of superior plants, and the study of similarities or differences between different specimens. Molecular markers are also used in germplasm management and marker-assisted selection (MAS) to increase the efficiency of germplasm breeding.
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Pre breeding is essential for linking genetic diversity arising from wild relatives and other unimproved materials to utilization. It is the main link between the germplasm conservation and its use in plant breeding. The major challenges of pre-breeding are lack of characterization, evaluation of genetic diversity, documentation of data; inter species relationship and strong breeding program and funding sources.
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Prebreeding has become quintessential in the present world of breeders. The improved lines have either been utilised to full extent or exhausted due to non conservation of the germplasm. Morphological diversity is to be used for developing mitigating new challenges.
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Genotype x environment interactions refer to the phenomenon where the performance or expression of a plant's traits is influenced by the specific environmental conditions in which it is grown. This interaction is crucial in plant breeding as different genotypes may exhibit different responses to various environmental factors such as temperature, soil type, moisture availability, and disease pressure. Breeders need to evaluate and select plant varieties that perform consistently well across a range of environments to ensure broad adaptability and stability of the developed cultivars.
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considering G x E interactions in plant breeding is crucial for developing cultivars with improved adaptability, stability, and performance across diverse environmental conditions. By understanding how genotypes interact with different environments, breeders can enhance the success rate of variety selection, tailor breeding efforts to specific environments, and deliver improved plant varieties that meet the diverse needs of farmers and consumers.
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Inbreeding depression occurs when closely related individuals are bred together over multiple generations, leading to a decline in the overall fitness and performance of the offspring. In plant breeding, inbreeding depression is a concern because it can result in reduced vigor, lower yields, increased susceptibility to diseases, and other undesirable traits. To counteract inbreeding depression, breeders often employ techniques like selective outcrossing or hybridization to introduce genetic diversity and restore vigor to the breeding population.
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The concept of inbreeding depression refers to the decline in the overall fitness and performance of offspring when closely related individuals, such as siblings or parent-offspring, are bred together over multiple generations. Inbreeding depression occurs due to the increased expression of harmful or deleterious recessive alleles that are normally masked in genetically diverse populations.
In plant breeding, inbreeding depression has significant implications. When plants are bred within a small, genetically similar population, the frequency of homozygous genotypes increases. Homozygosity can expose recessive alleles that may be associated with reduced vigor, impaired growth, lower fertility, susceptibility to diseases, or other undesirable traits. As a result, inbred plants often exhibit decreased overall fitness and performance compared to their genetically diverse counterparts.
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Genetic and Breeding (Maize)
Plant Breeding it's my DNA
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High-throughput phenotyping for cob damage in crops involves the use of automated and high-throughput imaging and analysis techniques to rapidly and accurately quantify the extent and severity of cob damage in a large number of plants.
One approach to high-throughput phenotyping for cob damage is to use imaging technologies such as visible and near-infrared spectroscopy, hyperspectral imaging, and thermal imaging. These technologies can capture detailed images of the plants and cob structures, which can be analyzed using computer algorithms to detect and quantify the extent of damage.
Another approach is to use sensors and other monitoring devices to track the growth and development of plants over time, and to detect any changes in cob morphology or other physical characteristics that may indicate damage. This approach may involve the use of non-destructive imaging techniques, such as X-ray computed tomography (CT), to visualize the internal structures of the cob and identify any signs of damage or disease.
High-throughput phenotyping for cob damage can also involve the use of machine learning algorithms and other data analytics tools to identify patterns and trends in the data collected from multiple plants. These tools can help to identify key variables that are associated with cob damage, and to develop predictive models that can be used to identify plants that are at risk of developing damage in the future.
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How can plant breeding and biotechnology be used to develop crop varieties that are resistant to pests and diseases, drought-tolerant, and have higher yields?
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Plant breeding and biotechnology offer promising strategies for developing crop varieties with desirable traits such as pest and disease resistance, drought tolerance, and higher yield. Here are some examples of how plant breeding and biotechnology can be used for crop improvement:
  1. Genetic engineering: Genetic engineering can be used to introduce specific genes into crop plants, such as those that confer resistance to pests or tolerance to drought. This approach has been used, for example, to develop Bt cotton that is resistant to bollworm and maize that is resistant to maize streak virus.
  2. Marker-assisted breeding: Marker-assisted breeding is a technique that uses molecular markers to select for specific traits during the breeding process. This can speed up the breeding process and reduce the time and cost of developing new crop varieties with desirable traits.
  3. Genome editing: Genome editing technologies such as CRISPR-Cas9 can be used to precisely modify the DNA of crop plants to introduce specific traits. This approach has the potential to create new crop varieties with desirable traits more quickly and efficiently than traditional breeding methods.
  4. Phenotyping: Advances in phenotyping technologies allow for the rapid and accurate measurement of plant traits such as yield, disease resistance, and drought tolerance. This information can be used to identify plants with desirable traits for breeding programs.
  5. Genetic diversity: Crop breeding programs can also use genetic diversity to develop new crop varieties with desirable traits. By selecting and cross-breeding plants with diverse genetic backgrounds, breeders can create new varieties that are better adapted to specific environments and more resilient to pests, disease, and climate change.
Plant breeding and selection may help to produce new verities of crops more adopted to climate change, these links may help you understand the topic:
More videos on breeding:
Breeding - repeatability of traits https://youtu.be/soxbOHf-mM0
Population parameters and breeding values explained: https://youtu.be/l_ePF9RTyts
How to calculate a Breeding Value: https://youtu.be/zvG3ychxX68
How to predict Selection response (Breeding and Selection)https://youtu.be/tikwKFU1riQ
Plants and Animals Breeding and Selection Methods-2 https://youtu.be/KROyOPvAjMI
How to calculate narrow sense heribtability: https://youtu.be/OkP7_xDuiig
What is selective coefficient and relative fitness: https://youtu.be/XeEx5Feeiq0
How to calculate hybrid vigor: https://youtu.be/yQVwSy1pFjQ
How to calculate hybrid vigor - 2: https://youtu.be/em7xuxtuDvg
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Hello,
I am working with maize drought tolerance. I am analyzing data under drought stress and normal condition. While doing data analyses I have found that traits like Anthesis silking interval, thousand kernel weight has a heritability of zero under drought stress condition. But when I combine data of both drought stress and normal condition heritability of those two traits is not zero. Is there any problem with my data? or is it explainable in other ways?
Looking forward to answers.. 
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Data quality check will be important as suggested by many: influential data points (high residual error), outliers... Check the yield reduction under stress and you may find that some data points are higher under stress environment compared to their counterparts under a well-watered environment (this is unlikely unless there is an issue in the well-watered environment). You may also try fit spatial models in your analyses to remove row, column and local variation effects.
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I am investigating the effect of environment on gca, sca and heritability degree. Line x tester = 4 x 5.
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There are several software options available for conducting line x tester analysis that can handle multiple environments, including drought and control treatments. Here are a few options:
  1. SAS (Statistical Analysis System): SAS is a powerful statistical software suite that is widely used in the field of agriculture and plant breeding. SAS can handle complex experimental designs, including line x tester analysis with multiple environments.
  2. R software: R is a free and open-source programming language that is widely used in statistical analysis and data science. There are several packages available in R for conducting line x tester analysis, including the "AGHmatrix" and "lineXtester" packages.
  3. GenStat: GenStat is a comprehensive statistical software package that is specifically designed for use in agricultural research. It includes a range of tools for analyzing complex experimental designs, including line x tester analysis.
  4. SPSS: SPSS is a statistical software package widely used in social science and other fields. It also has the capability to conduct line x tester analysis with multiple environments.
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How can you identify and isolate specific genes involved in crop yield or disease resistance?
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The answer to your question should be long. The disease resistance is usually conferred by a major gene while the crop yield is conferred by multiple minor QTLs/genes. Also, there are different approaches to isolating a gene of interest. There are several main steps to isolate the disease-resistance gene using a map-based approach.
1. Perform QTLs analysis using a small population like RILs, NILs, or DH that were derived from two parents carrying opposite genotypes.
2. Screen the plant carrying recombinants within the QLT of interest.
3. Develop internal markers to fragment the genetic window
4. Narrow down the genetic window based on the combination of genotype and phenotype of plants carrying recombinants.
5. Identify the list of candidate genes within the final genetic window
6. Validate the candidate genes to identify the actual gene conferring the trait of interest (using mutant analysis or gene transformation).
Good luck!
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Please describe your new idea.
How to improve the plant breeding research
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Many plant breeders are now using CRISPR technology to accelerate Plant Breeding research/program.
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Dear All
I have scored plant height and spike length in three replication in two years.
The analysis of anove was
genotypes+replicaiton+years+ R*G+R*Y+R*G*Y
I have found high significant correlation between the two years and no significant interaction G*Y
the correlation between 2021 and 2022 for plant height is 0.99. I really astonished how I have high significant differences between the two years in Ph and found such high correlation between the two years for pH. the same trend also was found for SL
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Correlation compares population means over the two seasons, the high correlation indicates a similar overall performance of the population. ANOVA compares the performance of each genotype between both environments. In your case genotypes behave in different ways in both environments; however, the overall performance is similar.
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In an experiment with 10 varieties of maize of 3 replications. The researcher taken the data of NCLB (Disease) incidence as score (0-9). In the scale, 0-9 are the percentage disease incidence on those treatments (varieties) viz.,
0- 0%
1- 1-3%
2- 3-6%
3- 6-12%
4-12-25%
5-25-50%
6-50-75%
7-75-87%
8-87-99%
9- 100%.
Taken this ordinal data! three times during crop cycle.
Now i want to know, among which treatments (varieties) there is statistical significant difference present in disease incidence and which treatments are statistically at par among eachother?
How can I calculate that?
Can I use Normal ANOVA for this non-quantitative data?
Thank you
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The Kruskal–Wallis test by ranks or Kruskal–Wallis H test (= one-way ANOVA on ranks) is a non-parametric method for testing whether samples originate from the same distribution (cited after: Kruskal–Wallis H Test using SPSS Statistics, Laerd Statistics).
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Dear All greetings! I was studying a paper describing combining ability. I found the word per se value, per se performance. I searched on google but did not get a satisfactory answer. Can anyone cooperate?
Thank you.
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The mean performance of the parents
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We are currently studying different genotypes for evaluation of panicle architecture. Can anyone suggests any softwares that can be useful for this investigation?
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Contour mapping software
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I want to grow cacao plant, but whats the best procedure to cultivate this plant?
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Researchers have quantified, for the first time, how the species living on small-scale cacao farms influence the production of one of the world’s most beloved foods. They set up a plot of cacao trees (Theobroma cacao), which provide the raw material for chocolate, and selectively excluded birds, bats and flying insects. The presence of any of the creatures increased the total amount of cacao grown. Trees that were accessible to birds and bats had more than double the yield of trees that were not...
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I couldn't see much paper where plant breeders use biochemical such as proline content Malondialdehyde (MDA) and dyes such as NBT or DAB( for ROS detection) for screening stress-tolerant accession on a large scale (100-200 which I suppose is possible to do). Are not these methods better than phenotyping grain yield, biomass, plant height, NDVI, LAI, etc?
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Acording biochemichal analysis, you can use but, depends of research facilities, I mean leader, investigators, experiments to follow, laboratories, and differents items you have to follow.
On the other hand it is much better to have and experimental parcels on the field in order to evaluate all and differents items for stress tolerence and get very good results.
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Genetic engineering should be seen as one of the many tools available for use by plant breeders to improve crop varieties so that we increase food production, control pests, and improve farm profits.
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Dear @Sravani Ponnam
Plant breeding, in its broadest sense, is the art and science of changing the plants genetically in relation to their economic use. Whereas, genetic engineering is the genetic manipulation (bypassing sexual reproduction) such that individuals with a new combination of inherited properties are established. Plant breeding is essentially a technology, and it utilizes various techniques. To my opinion, genetic engineering is the well utilised technique of present day plant breeding. Of course, many hold different opinions! The details can be accessed at:
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plant breeding and genetics
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Dear nishit
Negetive variance is not possible due to square value but it may be due to lack of random mating while developing half sib, sampling error but must be report because of arise due to very dmall megnitude of respective component.
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Some domestic oilseed rape genotypes express extra long seed viability (>80% are germinated after 13 years of storage at room temperature). Is it a genetically controlled trait? I know that hybrid seeds have shortest length of life, does this correlate with homozygosity? There is no references exept of a few ones devoted more to seed dormancy: http://www.nrcresearchpress.com/doi/abs/10.1139/b83-405#.U2w-yIF_unM, and seed coat properties: http://link.springer.com/article/10.1007/BF02857926/
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This work supports that seed longevity in oilseed rape (Brassica napus L.) is genetically controlled.
So far, such feature was found in several more Brassica rapa and B. juncea genotypes, and frequency of viable seeds suggests a recessive gene control.
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Genotypes: 138
Replications: 3
Site: 1
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Thanks, Vikas Kumar Singh. I will do that and see how everything goes.
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Dear colleagues,
I used the R package for line*tester analysis (Agricolae library).
It calculated the GCA effects, SCA Effects, S.E. (gca for line), S.E. (gca for tester) and S.E. (sca effect).
The experimental material comprises eight genotypes. Five genotypes were used as females (line) and three genotypes were used as males (testers).
The 15 F1’s and their parents were evaluated in a randomized complete block design with three replications.
I want to know the degree of freedom (T-test table) for performing the significance test of General combining ability (gi) effects and Specific combining ability (sij) effects.
Regards
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For significance test of both GCA (of lines and testers) and SCA (of crosses) effects, you should consider error degree of freedom, that is, (r-1) x (tr-1) = 2 x 22 = 44.
For further detals, you may go through:
Sharma JR. 1998. Statistical and biometrical techniques in plant breeding. New Age International Publishers, New Delhi, Pp. 138-152.
Singh RK and Chaudhary BD. 1996. Biometrical methods in quantitative genetic analysis. Kalyani Publishers, New Delhi, Pp. 205-214.
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Dear all,
I want to analyze a factorial split-plot in time using SAS.
Factorial Experiment using Completely Randomized Design (CRD);
Factor A: treatments (a1-a4)
Factor B: harvest time, different days after treatment (b1-b5)
Replication: 3
Does anyone have SAS codes for this analysis?
Regards,
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What is the advantages of estimating BLUPs for GWAS studies
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Dear Gregor Steve , great question!
There are different ways of using BLUPs for and from GWAS. However, there are also limitations!
Generally speaking, we want to use phenotypic records in all statistical analyses. But, sometimes this is not possible (for several reasons) and we may use BLUPs instead. Here are three common reasons for using BLUPs instead of phenotypes:
Example 1: Complex models
- Some software have limitations in performing some type of analyses, such as, but not limited to: including random effects other than the residual, repeated records (related to the previous one), multivariate analysis (aka multiple-trait analysis), etc.
- In this case, one could use BLUPs that are already adjusted for these effects when performing GWAS
Example 2: Limited phenotypes
- Given a genetic relationship matrix (e.g., A matrix, Genomic Relationship Matrix) that measures the genetic similarities (i.e., covariance) among individuals, the Mixed Model Equations (MME; Henderson, 1963) allows every single individual in the relationship matrix to have a BLUP, regardless of the individual having or not phenotypic records.
- Hence, when the genotypic data include individuals with and WITHOUT phenotypic records, a larger dataset used for analysis could be obtained by using BLUPs instead of phenotypic records.
Example 3: Individuals with large progeny records
- In general terms, when an individual in the relationship matrix has lots (i.e., hundreds to thousands) of progeny records, the BLUP of this individual should be highly accurate.
- Hence, in a large number of genotyped individuals have a large number of (non-genotyped/limited-genotyped) progeny with phenotypic records, the use of BLUPs in place of its phenotypic records could provide with better/more accurate GWAS results.
However, as I mentioned, there are also limitations about this approach:
Example 4: BLUPs have different accuracies
- When talking about real data, we see a large variation on the number and degree of relationships among individuals, the number of phenotypic records, and more.
- Therefore, some BLUPs should be more accurate than others. HENCE, the statistical analysis using BLUPs should be properly weighted by the level of uncertainty of these BLUPs.
- Such weighing procedure could be complex or impossible to be implemented (depending on the software and dataset)
Example 5: BLUPs are only part of story
- BLUPs are estimates of the additive values of the individuals. Thus, if your goal in your GWAS is to identify non-additive effects, such as dominance and epistasis, it is not expected to identify associations for SNPs with non-additive estimates.
- Therefore, BLUPs, unless specifically calculated to include those effects*, should not provide you with these associations.
*By the way, if non-additive effects are included, we shouldn't call them BLUPs anymore.
There are additional thoughts about this, but I think this could give you some clarity.
Please let me know if you have any other questions. Thanks, Nick
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Typically exposing plant parts such as seeds, stems, pollen grains etc. to radioactive isotopes (e.g. gamma radiation and x-ray) and chemical mutagens (e.g. ethyl methanesulfonate [EMS]) induce vital mutations for plant breeding programs.
BUT Natural radiation and microgravity in space induce genetic mutations for selection. Below is a summary:
  • Grow plants in the space station to maturity for one or few cycles.
  • Space grown plants are exposed to natural stresses (reduced soil moisture, nutrients, and carbon dioxide during reproduction).
  • This stress is induced by natural cosmic radiation (cosmic rays and effluvia from the sun) and earth’s gravity both enhancing genetic mutations.
  • Seeds harvested from space-grown plants can be grown on Earth to select novel mutants under glasshouse and field conditions for various traits (e.g. tolerance to drought and heat stress, resistance to insect pests and diseases, early maturity, flower colour, plant architecture, reduced plant height, improved gas exchange, better root growth etc.).
  • The new mutant varieties can be bred further or the seed deployed to farmers for commercial production.
Satellite missions and subsequent selections have produced some 200 improved crop varieties in China.
StarLab Oasis, a private organisation, is set up to raise plants in space for this purpose.
It reads like science fiction, but it is a fascinating, optimistic and complementary tool to conventional breeding.
I hope this service will be available for major crops in the near future. I cannot wait to send my seeds to the International Space Station (ISS), and I hope you do too.
The above read is extracted from
Shimelis Hussein
Professor of Plant Breeding
University of KwaZulu-Natal
South Africa
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Dear @Hussein Shimelis Yes, there are several reports and reviews that have thrown lights on space breeding:
I don't know whether the space bred varieties of crop plants have been tested in other countries. I doubt varieties bred under microgravity (zero gravity) will perform similarly under the influence earth's gravity. Moreover, the basic philosophy remains the same, that is, radiation induced genetic alteration which leads mostly to recessive and deleterious mutations. Although application of artificial mutagenesis to crop improvements started as early as 1928 by Stadler in barley, it never gained much importance in practical crop improvement. Even through site directed mutagenesis, not much has been achieved so far. If China has achieved so much in a short span (just in 3 decades), I call it highly impressive achievements! Let China share its space bred varieties to other countries to assess their performance!
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Dear All
I am writing this concern to find out if any author (as a corresponding author) belongs to one of developing countries and tries to submit his work in the Crop Journal (The Crop Journal | ScienceDirect.com by Elsevier). I have submitted twice and got a rejection from the editor without convincing reasons (your paper doesn't meet the standards of the journal)
1- The first rejection, I have contacted the handling editor providing many similar articles published in the journal in the same year (2020) and with fewer analyses than I did in my paper. His reply was, "submit it again and select another editor"). The same manuscript was successfully published in BMC Plant Biology
2- second rejection was in 2021. Imagine I submitted a new article (two years data, with three locations) and after three hours I got a rejection from the handling editor telling me the same silly reason. I am quite sure that three hours for a manuscript including many figures and tables and more than 6-8 supplementary files were not enough to make a fair judge on the paper. I resulted from it again asking the chief in the editor to assign another editor. After one house, I got the rejection by the same handling editor.
So, it looks to me that the editor just read the affiliation and decide to reject the paper without reading it carefully. I officially complain to the Elsevier publisher about this situation.
Does any face the same situation?
Your feedback is extremely important.
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Dear Amira M.I. Mourad I have had an opportunity to publish one of my reviews "Integrated physiological and molecular approaches to improvement of abiotic stress tolerance in two pulse crops of the semi-arid tropics" in the Crop Journal (Vol 6, No. 2). Although the review process was highly rigorous, the handling editor seemed to be cooperative. However, at that time, the journal was devoid of "impact" factor. Soon after, it received an impressive impact factor, and thereafter, it began to charge publication fee. Because of high publication fee, I did not even think further to submit any paper to this Journal.
That what you have narrated happens sometimes with most authors. Sometimes, even for simple reasons (such as language, grammatical mistakes, etc), the editor decides to reject the submission. For authors, these may be simple reasons, but for the editor these may become the major ones. Moreover, the number of submissions and acceptance rate also account for such rejections. To my knowledge, the journal is an official publication of the Chinese Academy of Agricultural Sciences; I don't think the handling editor can practice any bias towards authors belonging to developing nations.
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Dear colleagues,
I used the R package for line*tester analysis (Agricolae library).
It calculated the General combining ability effects (GCA) of parents, but I don't know how to calculate the significance of the results.
In addition, I want to Estimate Narrow sense heritability and Heterosis (Better Parent (BP) and Mid-Parent (MP)) for hybrids by R.
Does anyone have a solution?
Regards
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Dear @Mostafa Modarresi Please go through below mentioned books:
1. Biometrical methods in quantitative genetic analysis by Singh, R. K. and Chaudhary, B. D., Kalyani Publishers, New Delhi
2. Quantitative Genetics in Maize Breeding by Arnel R. Hallauer, J. B. Miranda Filho, and Marcelo J. Carena, Springer
In addition, you can also access a paper of your interest. Hopefully, you can find solution to your problem.
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What open-source softwares are available to generate a heat map of the Jaccard similarity coefficient for SSR diversity from the available data set?
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```r
nc <- ncol(dat)
out <- matrix(0, nrow = nc, ncol = nc, dimnames = list(colnames(dat), colnames(dat)))
Jaccard <- function(x, y) {
i_len <- length(intersect(x, y))
u_len <- length(union(x, y))
return(i_len/u_len)
}
for (i in seq_len(nc)) {
for (j in i:nc) {
out[i, j] <- Jaccard(dat[, i], dat[, j])
}
}
pheatmap::pheatmap(out)
```
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Hi,
I'm new here so sorry If I'm in the wrong place.
I've been working in UK Agriculture for around 10 years now and I've decided to pursue a career in R&D and plant breeding after working for a seed production company with a local breeding site. I found it all fascinating so I've started a biology degree at Open University and I'm hoping to get a job as a technician or something similar soon.
I know I'm still a fair way from my ultimate goals but in the mean time I'm looking for any good books that go into detail on agricultural plant breeding methods, plant biology, genetics and modern techniques such as Marker Assisted Selection, Doubled Haploid and tissue culture.
I'm looking for something that covers these subjects in detail but on a suitable level for a student (I like diagrams and pictures haha). One of the best looking ones I've found so far is "Plant Biotechnology and Genetics: Principles, Techniques, and Applications" by C. Neal Stewart Jr. But i could do with some advice from anyone who's read any books like this in the past.
Thanks
Stuart
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Greeting to all researchers,
As we all know that YVMV in okra is not yet all seed transmitted viral disease, so, what happens if we go for harvesting of those infected plants. because, breeding for viral disease resistance is not only concerned trait.. so, the question arises can we go for harvesting of YVMV infected plants for generation proceeding?
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To me, big no, viral diseases are systemic so harvesting already infected pods could be a source of contamination for the new progenies.
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CMS lines are important in breeding programs. Crossing CMS with maintainer line maintains CMS lines.
But How lines with heritable Male sterility Characteristics are produced initially?
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Dear Radhakrishna Bhandari Cytoplasmic male sterility is controlled by the cytoplasm (called sterile cytoplasm), but may be influenced by nuclear genes. It may result when a plant with recessive nuclear genes has defective mitochondria due to mutation. The sterile cytoplasm often results from the introduction of nuclear chromosomes into a foreign cytoplasm. In past, CMS lines in sorghum were produced by placing "kafir" genome into "milo" cytoplasm by pollinating a milo plant with kafir pollen, and successively pollinating the progeny as the female back to kafir as the male, until the entire kafir chromosomes was recovered. The same procedures were followed to obtain CMS lines in wheat, rice and pigeonpea. The details can be found by accessing books by John Milton Poehlman, and RW Allard.
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Is there anyone empirically investigating agrobiodiversity loss? Specifically I'm asking about Crop Genetic Diversity and thus not the loss of cultivars by itself or the variety of cultivated crops. Accordingly, the loss of genes (or traits) can be prevented by Ex-Situ conservation.
I found only one source empirically investigating crop genetic diversity loss and this was stating that on the gene level, there is no loss for the 8 crops investigated (Van de Wouw et al. 2010).
Then there is this famous FAO-Statement: «Since the 1900s, some 75 percent of plant genetic diversity has been lost as farmers worldwide have left their multiple local varieties and landraces for genetically uniform, high-yielding varieties.» However, I couldn't find any empirical support for this statement. And this document links to a paper about women's role for agrobiodiversity, but nothing about agro biodiversity loss.
Can anyone help?
References:
FAO (n.d.): What is happening to agrobiodiversity? Online available under: http://www.fao.org/docrep/007/y5609e/y5609e02.htm [Accessed July 5, 2013].
FAO. 1999b. Women: users, preservers and managers of agrobiodiversity. http://www.fao.org/sd/nrm/Women%20-%20Users.pdf
Van de Wouw M., Van Hintum T., Kik C., Van Treuren R., & Visser B. (2010): Genetic diversity trends in twentieth century crop cultivars: a meta analysis. In: Theoretical and applied genetics., vol. 120:6, 1241–52.
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Dear colleagues, I’m thrilled to get to announce that we have finally published a major review of crop genetic erosion, which is open access here: https://doi.org/10.1111/nph.17733. Thank you for this thread, which was useful toward this effort. Regarding the mysterious origins of the “75% of crop diversity lost in the past century”, please see Box 1 in the article. This represents our ongoing work to clarify the origin of this narrative. Spoiler – we still aren’t sure of the origin, but we have some good ideas about where it may have come from. Thank you again. Please pass this paper around.
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Several stability statistics can be used for evaluating the mean performance and stability of genotypes across a set of environments. Aiming at improving the R package 'metan' <https://tiagoolivoto.github.io/metan/> I would appreciate know from the community which are the most used stability methods, and if someone knows or use a method not yet implemented in open-source software. If you use R to compute stability statistics, what is the main difficulty you face? Which would you expect from an R package for multi-environment trial analysis?
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Our study has just been published in volume 113 of the Agronomy Journal, part of the American Society of Agronomy. Another practical contribution to multivariate selection for mean performance and stability in plant breeding trials. Full-text is available at https://bit.ly/2ZaAsZG and supplementary material with example codes at https://bit.ly/2Za3c4N. Thanks to Maicon Nardino, Daniela Meira, Carine Meier, Diego Follmann, and Velci Queiroz de Souza for the collaboration!
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There is very little publication where functional characterization(cloning, overexpression, silencing, etc.) of genes identified through GWAS has been performed. However, most of the publications on functional characterization are on genes identified through transcriptome. Why is this? I doubt whether there is any usefulness of GWAS on crop improvement or not? if yes then give me some successful publication examples?
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The reason is that there are very few publications where functional characterization (cloning, overexpression, silencing, etc.) of the genes identified through GWAS has been performed. However, most of the publications on functional characterization revolve around genes identified through transcription because these are quantitative traits and are controlled by many genes and the influence of the environment is very high and the effect of each gene is weak (Minor genes) and they have small-effect genes rather than Major genes that have large -effects
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good morning
Through this scientific initiative, I would like to present an initiative to form an international scientific team among us to develop joint ideas and research from the brothers who are sick and working in the field of cotton. From the field of plant breeding, genetics, physiology and agricultural transactions.
Waiting for your responses
The approver puts the name, institute, university, country, and email
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This is loudable idea, I am not currenting work on cotton but I have a friend who is working on it. I will recommend this to him in order to get in touch with you. Hope my suggestion help. All the best
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Large germplasm collections may have several duplicates which need to be removed from the set. DUS characterization based on morphological traits are the best way to differentiate them. But it is very tough to DUS characters in large germplasm lines. Is there any method in molecular breeding to identify the duplicates in huge germplasm collections of rice? Kindly give your opinion and suggestions.
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You could use SNP, PCR, molecular markers, and other molecular methods to differentiate the DUS Characteristics in large germplasm lines.
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Turmeric is a polyploid. Mostly sterile. However, it sets seed occassionally. I have collected seed and kept in poretrays filled with cocopeat. I have not observed any germination even after 15 days. Yesterday I have kept some seed on germination paper spread in a petridish. Can any one tell me what is the viability and time taken for germination of turmeric seed. Seed was collectd from inflorescences fifteen days ago, the flowering was complete about five months ago. Seeds were within the inflorecences for about five to six months before collection and sowing.
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Seed set is observed in turmeric (Curcuma longa L.). Both triploids and tetraploids are available in cultivated turmeric. The turmeric seeds are arillate which have two seed coats, the outer thick and the inner thin. Staggered germination is observed, usually commences after 20 days after sowing and continued for several months. The percentage of seed germination varied with cultivars, and even plant to plant.
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As SSR are most widely used markers in major cereals. They are highly reliable (i.e. reproducible), co-dominant in inheritance, relatively simple and cheap to use and generally highly polymorphic. What other factors can affect polymorphism in case of SSR markers?
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The polymorphism at DNA level depends on several factors of which genetic divergence of the parents or the genotypes under study is one of the most important one. Secondly, as very rightly mentioned by Ricardo Julian Licea-Moreno sir, it requires detailed information about the genotypes and the markers you are using because it is possible that many of those markers may belong to the same region of a specific chromosome.
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In between Genetic and plant breeding, plant physiology, agriculture biotechnology and plant pathology which one subject have maximum contribution in crop improvement? Which one is better for research?
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Among the research areas of Genetic and plant breeding, plant physiology, agriculture biotechnology and plant pathology all of them have contribution in crop improvement but I thing Genetic and plant breeding has maximum contribution in crop improvement. Agriculture biotechnology is also better but it has to follow conventional breeding.
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Hii, please give me a complete guide or any material to analyse the Experimental data of RBD, CRD etc. to a find Genetic diversity, Character association, Path analysis & other Plant Breeding related experiments by using IBM SPSS
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Dear @Rajasekhar Chowdary Duddukur As suggested by @Suyash Bhimgonda Patil, I also suggest you to access online portal of OP Stat for analysis of genetic diversity, character association, path coefficient analysis and other plant breeding experimental data. It is useful, and I have used it several times.
Best wishes, AKC
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I am from Nepal and we lack adequate resources/ infrastructures for advanced researches in this field. Also, I do not have sufficient knowledge at present to pursue these researches independently. I am willing to devote a lot of time if given the opportunity to collaborate.
I hope to get some updates regarding the ongoing researches in Nepal (in Plant breeding/ genetics.)
Thank you!
Happy learning!
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Dear Amrit Sharma, although the scientific bases of plant breeding are the same, doesn't matter the species, the objectives respond to local particularities and necessities. Focus your attention on regional and/or national bottlenecks and problems. Depending of what are you pursuing, in some cases you wouldn't need too much resources. Observation is an important part of research, and selection is likely the "stronger" allied for plant breeders. Of course, in other cases you would need potent scientific infrastructures to deep in your research field, and for to obtain faster advances. However, your capacity to adapt to your conditions as well as to optimize the use of your resources might give you many satisfactions and advances to reach your goals.
Good luck!!!!
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Which one software is best for agricultural data analysis?
Experimental design
Plant Breeding Trials
Quantitative Genetics
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R is free and has high flexibility in most genetic and statistical analyses.
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I use the R package agricolae a lot in my work but it doesnt offer the option to design an Alpha-Lattice experiment. There are a few packages on how to calculate means from experiments like this but I can't find packages to set up trials like this. Any suggestions
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can anybody help me to brief about how to do stability analysis in plant breeding, based on wheat rust data how can i do the stability analysis for wheat rust resistance and how much time it take to do that
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AMMI and GGE biplot analysis can be used
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In your opinion, what plant breeding plant can be important and useful in the future?
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Have a look at our publication on edible plants, where we recommend a number of promising plant species to improve people's diet and livelihoods:
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Considering the present situation of the world for a safer future, In which direction plant breeding research mostly require to focus? Quantity(higher productivity) or quality (more nutritious)
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T.R. Das Should be focus on both. For example, China is developing super hybrid rice to increase yield. But they also need to use some disease resist elite lines to do that. So, the final product can be disease resistance and high yield production.
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Now a days, speed breeding is becoming famous for rapid varietal development through rapid generation advancement.
However, it's applicability and feasibility for everyone and everywhere is still not wider. What you think about it? How will it be useful in future breeding for crop improvement.?
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Dear Parmeshwar K. Sahu Any breeding scheme based on rapid turn over of generation cycle, ultimately resilting in rapid attainment of final outcomes may be referred to as speed breeding. Its concept is rooted in haploidy breeding through which completely homozygous lines are derived from haploids by using colchicine in a single generation. Recently Australian breeders have used it to develop a wheat variety "DS Faraday". The details of speed breeding can be accessed at the links goven below:
Thanks!
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Dear all
I am wondering what is the advantage of multi response models of the form
trait1,trait2 = Genotype + Environment + GenotypexEnvironment
Where trait1 and trait2 are traits that are measured at the same experimental unit, e.g. yield and protein content of a certain plot in a cultivar trial.
The GNU R package sommer offers in its function mmer() the possibility to specify such a model.
What I had expected, is that the residuals (for trait 1 and trait 2) after such an analysis are uncorrelated since the information of the covariance is used to have a more precise estimate for the effects in the model (BLUPs, variance components).
However, for simulated data with correlated experimental error, I see no difference/advantage of such multi-response models.
So what are the advantages? Does anyone have an idea?
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This answer is not referring to my question.
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Kindly suggest the name of free softwares available for LxT analysis and heterosis estimation in crop plants.
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You can run a Line x Tester Analysis
Directly, online
Use the link
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Please recommend some simple software that is available in free for analysis of different conventional breeding experiments
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Dear Punam Bhattacharjee,
You can use R software for any kind of breeding analysis.
Kindly visit the CIMMYT website, you will find several free and easy software for analyzing phenotypic and molecular data.
I recommend META-R for multi-environment trials and Rindsel for disease data.
With best regards.
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Dear all, Please suggest the plagiarism checker software which is 100% free and also don't have the restriction of word limits.
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I use duplichecker. Google it.
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Scientific community is now facing a new threat of unethical publications in predatory journals.
How someone can identify the predatory journal?
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Pankaj - it is not too difficult to recognise predatory journals. One just has to know the 'classic' and common signs of a predatory journal. They don't necessarily expect to exists for long - so they often use 'minimal effort for maximum financial gain' - and therefore there are often many errors and of poor quality - at least in parts.
There are certain factors then that make it reasonably easy to identify predatory journals - or at least ‘cause doubt’ so further investigation is required. Essentially, rule number one with predatory journals is always be very wary when any journal approaches you directly. If you are not sure - then the Directory of Open Access Journals (DOAJ) is a good site to check first. It lists the 'good' journals. If the journal targeting you is not on their list - it's another warning sign. Then there are many other warning signs - such as:
· Poor quality online interface.
· Minimal and/or very broad journal scope i.e. we publish almost anything related to a whole discipline.
· Poor English quality and grammatical errors.
· Check the country of origin. Many predatory journals are based in certain countries (usually sub-continent). Some, however, will try to give the impression that they are based elsewhere i.e. US, Europe.
· Unknown editors.
· Unknown or no editorial board and/or all based in the country of origin
· Unsophisticated online manuscript submission processes i.e. send a word document by email
· Upfront publishing charges
· Not registered or associated with any reputable professional bodies and/or citation agencies - nor are credible Impact Factor sources stated. Articles may not have been assigned a Digital Object Identifier (DOI) number.
· Check the quality of existing articles in the journal. They are usually a very mixed and poor quality.
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Scientific community is now facing a new threat of unethical publications in predatory journals.
How someone can identify the predatory journal?
Is there any authenticate list of predatory journals available?
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Borrowing from researcher César Jiménez-Yanez:
Regularly predatory journals have the following characteristics:
Send emails regularly inviting to publish
They offer impact factor and show medals and high indicators
Offer to publish in a very short time (days or weeks)
They are multidisciplinary, that is, they accept jobs from all areas and disciplines.
Charge to be published (high costs)
They do not detail the arbitration process (sometimes they do not even mention it)
Present the DOI or Crossref logo as something "important"
The name of the magazine regularly looks like a serious academic journal
They are not affiliated with COPE (https://publicationethics.org/)
You can check the ISSN of the magazine in the MIAR database http://miar.ub.edu/
You can check the ISSN of the magazine in the SCIMAGO database https://www.scimagojr.com/
You can check the ISSN of the magazine in the Clarivate Analytics database http://mjl.clarivate.com/
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how can linkage disequilibrium used in plant breeding
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In 2016 I have developed a set of mutants in two varieties of turmeric-Mydukur and Prathibha. Now, in M1V4 evaluation, have identified a few are performing better than the parents. I am going to analyze quality parameters in these selected lines. I have sown M1V5 (selected mutants) along with parents this 2020-21 season. From here, I want some of these to be developed and released as varieties. Whate are the steps to be taken from here. Please help me.
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Hello Kalidas,
You can take these selected mutants and grown under different environmental conditions with different locations to test their performances and selected the best variety that has not change its performance.
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Seeds obtained from heterozygous T0 mutants obtained through CRISPR CAS9 were sown and the plants raised were properly genotyped. Following normal segregation pattern, T1 generation had WT, heterozygous and homozygous plants. The homozygous were not producing normal seeds but strangely few (2 in many) produced more than 50% normal seeds, when those seeds and the plants raised from them were genotyped they were found to be heterozygous. In T1 I ignored this (thinking that T0 plants from CRISPR CAS9 might carry chimeric mutation for the gene).
To get T2 generation, seeds obtained from T1 heterozygous were planted, but T2 homozygous plants also had such plants. The situation even continued to the next generations. I am wondering what might be the cause. I know that cross pollination in rice occurs to small extent but if that is the cause then all homozygous plants must bear small number of such seeds. In this case homozygous usually don’t produce normal seeds at all, unusually few homozygous bear normal heterozygous seeds. Your suggestions will be highly appreciated to explain this situation. Thanks.
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You should check the availability of Cas9 structure also. If the Cas9 still in your homozygous plant (consider the target gene), you can have other heterozygous progenies.
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Can anyone help to confirm whether I am using correct stages for Chickpea crossing? I am using hooded bud for crossing and half open flower to collect pollen (please find attached pictures for stage). 
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R1
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I feel, highly focused conventional breeding methods give better target results than a MAS exercise with all the cost:benefit worked out. Maybe, there would be a time loss in conventional breeding, but we end up in a highly adapted concrete product, even though this point may be debatable. In that, MAS can give better results. But looking at an average plant breeder's lack of laboratory, money, skill etc type of resources, will it not be prudent to slog rather than be protocol driven? The practical learning that conventional breeding gives is unparalleled. Of late, there has been a significant shift of even conventional breeders to some molecular breeding aspects. Good, if the target is well defined and the work plan gives a better result. But again, look at the voluminous data being produced and published in the molecular breeding field as against conventional breeding, and it now longer has remained a level playing field. Journals are also attracted towards molecular breeding papers. Where are we heading?
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My suggestion is never to compare. Try to do best what resources available with you. Only thing is that you fix your target and dedicate for it. However, If you have both facilities then use both because both have their own value.
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In Jamun there is no phenotypic differences in the growth of both nucellar and zygotic seedling also the point of initiation of these seedling are also same.
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I think , this is the easiest method followed in all polyembryonic crops . Use of molecular markers is really difficult , where such mass scale production is required....
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Software should be able to phenotypic as well as marker genotypic data analysis. Kindly suggest probable price and the authentic source from where it can be purchased.
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STAR, PBTools and CropStat are free available and very user friendly softwares for various types of field data analysis. These softwares require minimum data formatting and IRRI also provides user manuals. Easy to learn and work. Try them.
This is the link
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Different chemical mutagens are available with different modes of action.
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EMS
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What is the best statistical analysis software for agricultural sciences especially for plant breeding & agronomic research?
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I prefer minitab as i am working a lot with this program and used to it every month there is updates regarding parameters: variabilities/means/normalities/ANOVAs etc there is also SPS : equivalent to SAS and it is also very good in performing statistics estimations regarding variations, and clusterings in Agriculture fields greenhouse trials, lab tests, control and treatment tests etc ...
for R I use it one time in genomic modeling (wheat crossing estimations) :D it was HORRIBLE trying it I have just understand that minitab can do what R do (almost) esecially when you want to compare big data (more than 10 ... variables/factors) and you deal with cofficeint and componenets ''multivariable"" and minitab or SPS/SAS can do it too
NEVER try Phyton though
best of luck
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As we do not know how many markers are required for screening of background during marker assisted breeding and if we cover the whole chromosome with marker still it will not impart accurate results. According to me intermittent phenotyping is important aspect in MABB.
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Several parameters require to be optimized in a marker aided background selection program viz. at least a few hundred highly polymorphic and evenly distributed markers with a reasonable genome coverage and a minimal marker density, no dependency of the markers to genetic background, minimum number of individuals for detecting recombinants in a given marker interval, and minimum number of data points to achieve fast completion of backcross program.
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Any free software for the design of experiments for agronomic and plant breeding data??
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