Plant Breeding - Science topic

Plant breeding for sure because the scale of production can outpace the number of plants that die from disease which equates to more cash flow in that industry.

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.

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.

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.

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.

@ 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.

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.

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.

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.

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.

The concept of hybridization in plant breeding involves crossing two genetically distinct parent plants to create offspring with desirable traits. It is a controlled breeding technique that combines the genetic material of different plants to produce new varieties with improved characteristics.

In hybridization, the male reproductive organs of one plant, called the pollen donor or male parent, are used to fertilize the female reproductive organs of another plant, known as the female parent. The process can be accomplished through natural pollination, assisted pollination, or even by manual manipulation of the reproductive organs.

The resulting offspring, known as hybrids, inherit a combination of genetic traits from both parents. This often leads to offspring with enhanced qualities such as increased yield, improved disease resistance, better adaptation to specific environments, altered flowering time, or enhanced nutritional value. By carefully selecting and crossing parent plants with desirable traits, plant breeders can create hybrid varieties that outperform their parents and meet the specific needs of farmers, consumers, or industries.

Hybridization is commonly used in various crops, including grains, vegetables, fruits, flowers, and ornamental plants. Hybrid varieties are typically designated with an "F1" label, indicating the first generation resulting from the cross between the two distinct parental lines. These F1 hybrids often exhibit a phenomenon called hybrid vigor or heterosis, where they display superior growth, yield, or other beneficial traits compared to either of the parent plants.

Hybridization plays a crucial role in modern plant breeding programs as it allows breeders to efficiently introduce and combine desirable traits from diverse genetic backgrounds. This technique has significantly contributed to the development of high-performing, disease-resistant, and commercially successful plant varieties.

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.

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:

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

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

GMO means Genetically Modified Organism and specifically modified by genetic engineering where genes from a different animal or plant are put into another. So “non-GMO” seeds just mean that genetic engineering wasn't part of the plant breeding process. GMO means Genetically Modified Organism – and specifically modified by genetic engineering where genes from a different animal or plant are put into another. So “non-GMO” seeds just mean that genetic engineering wasn't part of the plant breeding process. Non-GMO means a product was produced without genetic engineering and its ingredients are not derived from GMOs. Non-GMO Project Verified additionally means that a product is compliant with the Non-GMO Project Standard, which includes stringent provisions for testing, traceability, and segregation. The key difference between GMO and transgenic organism is that GMO is an organism that has an artificially altered genome, while the transgenic organism is a GMO that has an altered genome containing a DNA sequence or gene from a different species. Many GMO crops are used to make ingredients that Americans eat such as cornstarch, corn syrup, corn oil, soybean oil, canola oil, or granulated sugar. A few fresh fruit and vegetables are available in GMO varieties, including potatoes, summer squash, apples, papayas, and pink pineapples. Unnatural as the process of genetically modifying food may seem, GMOs do present a few advantages, compared to organic or conventional / non-GMO products. First of all, they are usually cheaper than their organic or conventional / non-GMO counterparts.

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.

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:

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!

Many plant breeders are now using CRISPR technology to accelerate Plant Breeding research/program.

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.

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).

The mean performance of the parents

Contour mapping software

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...

This respected portal (i.e. RG) defines plagiarism as: The term “plagiarism” has different meanings, but it usually includes copying somebody else’s work without permission.

I may be somewhat old-fashioned, but please have the following golden rules on how to avoid plagiarism, especially Self-Plagiarism:

Finally, believe me, or not: If you make one plagiarizing, you may solve one problem and fall into many others where some of which may be described as a knockout. Again and again, please always remember that if there were accusations of plagiarism, it is not well for any researcher's reputation, in any meaning.

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:

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.

The biochemical analysis are important, but if you have plant breeding programme should be more interesting and effective to make evaluation on the field. In those conditions you can evaluate and select the best plants for abiotic stress plants, and maybe to others topics like vigorous plants, also for production and quality. Then, you can select the most promissed material for your proposed objectivesz.

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.

Thanks, Vikas Kumar Singh. I will do that and see how everything goes.

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.

Dear Beitollah Amanzadeh , , Pamela Carvalho-Moore , Andrew Paul McKenzie Pegman Barbara Sawicka , Seyed Hamid Ahmadi

Thank you

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:

- 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

- 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.

- 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:

- 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)

- 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.

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

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!

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.

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.

```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)

```

Dear Dr. Stuart Dobson

Be so kind to see the following Scientific Investigation, thank you:

To me, big no, viral diseases are systemic so harvesting already infected pods could be a source of contamination for the new progenies.

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.

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.

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!

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

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

You could use SNP, PCR, molecular markers, and other molecular methods to differentiate the DUS Characteristics in large germplasm lines.

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.

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.

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.

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

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!!!!

R is free and has high flexibility in most genetic and statistical analyses.

see also:

and the R script in attachment

AMMI and GGE biplot analysis can be used

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:

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.

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!

This answer is not referring to my question.

Dear Parmeshwar K. Sahu

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.

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.

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/

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.

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.

R1

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.

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....

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

http://qgb.irri.org/products

EMS

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

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.

Manuel, RStudio tiene un conjunto de paquetes estadisticos que te pueden ayudar dependiendo de los que quieras analizar ... Yo lo uso para interacción genotipo por ambiente y permite un análisis adecuado...

Hey Mohammed Shaker Hossain; After conducting all the necessary trials which involve Stage I, Stage II, Preliminary Yield, advanced Yield Trials, Multi-location trials for the target traits of interests, You need data on DUS which is Distinctness, Uniformity and Stability for this variety and it is supposed to be different from the existing varieties that have been released before. You can even go further to genotype it to develop it's fingerprint for reference in case you want to do a QC/QA.

Dear Pak Haksong,

Yes, I know this, but for me this is totally new area. I have not enough knowledge from where and how I need to start. I read few article about this topic. Really it is so much applied and beneficial for plant improvement.

So, my request to you, if possible please give little space in your lab, so that I learn the whole process.

Try this link very useful.

https://plant-breeding-genomics.extension.org/estimating-heritability-and-blups-for-traits-using-tomato-phenotypic-data/

European Research Council (ERC) supporting top researchers from anywhere in the world:

https://erc.europa.eu/news/PoC-recipients-2019-third-round