What is a Gansta Dog? - Page 8

Pedigree Database

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mrdarcy (admin)

by mrdarcy on 20 May 2016 - 16:05

Names? Page 2 there are names and on a few other pages dogs are mentioned.

susie

by susie on 20 May 2016 - 17:05

Prager: When you breed two dogs, the art is then to match fault with quality where the quality is genetically dominant. That quality then overcomes the fault. It is about compensations for faults with pluses.

"Gangsta" is no "quality", but "hard" is.

As soon as we believe in simple genetics, "gangsta" bred to "too soft" statistically will NOT result in hard dogs, but in some "gangsta" and in some "too soft" ones ( Why should I breed a "too soft" German Shepherd dog at all? A honest question for the breeders out here...)

As example: Breeding an oversized dog to an undersized dog statistically won´t produce medium sized dogs, but some oversized dogs, and some undersized dogs.
The result: No puppy I want to keep...

In genetics 1 + 2 is NOT 3
but
1 + 2 is either 1 OR 2

That said, I have to use a dog with traits I want to reproduce, not a dog with "too much" or "not enough". I´d use the "hard" dog, not the "gangsta".

Gigante

by Gigante on 20 May 2016 - 17:05

Hundmutter 

Shades Smile

For most things its easy to follow in context like you said but there are terms and words that still make no sense to me. Im guilty of producer re writing the script often, but it seems inherient in the dog world.

 


Prager

by Prager on 20 May 2016 - 20:05

Susie OMG! Please try to read what I am saying. 
1/who is talking about breedign dogs who are  too soft? NOT ME!!
2/gangster dog is a dog and not quality but he has qualities( Plural)
3/you totally missed the concept of compensation with good for bad during breeding ( which I call dove tailing -oh forgive me another yet bombastic hansism) in breeding of dogs which I was talking about.
4/And in genetics 1+2 is not 1 or 2 . such simplistic thinking disregards Mendelian laws and all other laws of genetic. A trivial Punnett squares can convince a 1st grader otherwise.
5/ The last point you made of course ignores that there are no perfect dogs. Common mistake of novice naive breeder. Bless your heart.


by Gustav on 20 May 2016 - 20:05

Grappo v Leiperheimermoor and Wasko Von Eifelgrund were dogs that I considered " gangsta", both dogs were hard dominant dogs that would not submit to anything not on their terms....though they would work with handler on mutual terms if handler was skilled enough. They were both so unstable as to qualify and do well in national Sch 3 trials.

Prager

by Prager on 20 May 2016 - 21:05

LOL I like this game.
Gero z Blatnskeho zamku was so unstable that he was 2 X in Czech nationals, ZPS1, OP1, SchH3, IPO3, and got ZVV3 which was rewarded to about 3=6x per year.

And Norbo BenJu was so unsatbel that he was 3x participant WUSV, 3x FCI, SchH3, IPO3, ZVV2.....

 Both dogs were so unstable that they were bred  10s of times if not well over hundred of times and are in pedigrees of many renown Czech dogs and pedigrees of breeding programs of serious breeders  all over the world. 


by Ibrahim on 20 May 2016 - 22:05

I am only asking a honest question.
I heard gangster dog 1st time in my life from Matin when he said old breeders kept a gangster to boost drives in their dogs, but this dog was hidden, kept away and not shown to the public, If the gangster dog was stable then why wouldn't those breeders show it to the public?

One more question please, I understand, Prager, what you're saying about compensation.

Tell me what you think of the following:

You can produce hard dogs from one hard parent or two hard parents, using a gansta is not the only way around. So why risk getting few gansta dogs in a litter when you can get hard dogs and nil ganstas (when using a hard parent not a gansta one) ?


susie

by susie on 20 May 2016 - 22:05

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  

Punnett-Square for beginners, it´s based on the theory of dominant/recessive inheritance, fits well, because Prager talked about "genetically dominance" in the "gangsta" male and the "biddeble" female.

"So now when pups are born there is a high probability that some pups are going to be very hard but very biddable ."

In case you are breeding for something ( the example was breeding for "hard, biddable" dogs ) the best chance to get dogs of this kind is by using dogs of this kind ( hard, not "gangsta" ).

Take a look at the Punnett square, whatever you try to achieve, you have to deal with the given genetics, very simplified said :trait 1 + trait 2 does not result in trait 3, but in combinations of 1 and 2, visible will be 1 or 2, whatever is dominant, any "3" would be a spontanous mutation.

Oversized dog (1) bred to undersized dog (2) statistically will result in either oversized (1) or undersized (2) dogs, not in mediumsized (3).

1+2 (sorry, typo ) is not 3 ... Am I able to convince 1. graders now?

Oh, and, yes, I know that there is no single gene for "gangsta", and that there is no single gene for "hard", genetics are far away from easy, but I guess the narrower you stick to the goal you want to achieve ( in this case "hard" ) the better the chance to get a good result.
 


susie

by susie on 20 May 2016 - 22:05

Just read your post, Ibrahim Teeth Smile


by Gustav on 20 May 2016 - 22:05

Because the phenotype ( outward expression) of the dog,is not the only part of what good breeders consider when breeding....the genotype is also very important. There are these type dogs that I would never breed to because of genetics, otoh, there are some that I definitely would.
Breeding, to my way of thinking is so much more complicated than only breeding on phenotype.
I really wish this term had never been introduced to forum....it is difficult for some folks to grasp the understanding of this type of dog....especially if never really exposed. I have heard this term referred to by some trainers that were giants in sport and LE work. People who have forgot more than I know about working/training dogs.......i.e. One of the Martin brothers as Ibrahim attests. That's why I would never try to convince any of you about this type dog.....if you think it's a fabrication or useless to the breed....so be it.






 


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