Mating Designs In Plant Breeding

Use of Different mating designs in plants breeding

Plant breeders use various mating designs for plant improvement. Choice of good mating design is an important factor, which determines success in breeding plants. Mating design develop progenies for evaluation. Progenies in a mating design have relationship among relatives that have known genetic variance. For estimation of this variance, progenies produced through mating designs are tested in variegated environments in different experimental designs (Hallauer).
Mating designs objectives:
 Mating designs assess quantitative characters and estimate genetic parameters like additive and dominance variances
 In short term breeding, mating designs help in selection strategies
 Different hierarchical

Parents that are crossed to form progenies for evaluation are chosen either from germplasm or after rigorous screening of selection material. There are certain deciding factors that contribute in choice of good experimental mating design to form progenies. Mostly selection depends upon: study objective, crossing techniques, presence of sterility system, population size and type of pollination of plant species.

a. Crop reproductive system:
Type of crossing that occurs in crop plant is also a limiting factor in mating design for producing specific relatives. For example, polycross mating design is more successful in cross pollinated species, especially in plants having very small sized flowers that are not easily emasculated. Likewise North Carolina Mating design II, sometimes referred as polyandrous design is not very successful in plants having a single flower (like Maize). Similarly LineTester has major application in animal breeding.
b. Study objective:
Some times purpose of study is only initial screening of crosses produced in specific mating pattern but often objective is to serve dual purpose i.e. finding genetic parameter combined with selection in progenies.
c. Estimates

Either these are dismissed or calculated in a proper way to avoid biased estimation (over estimation or under estimation). A good design must have inbuilt capacity to calculate these factors and provide reliable estimate.
Causes for occurrence of negative variances:
In analysis value of covariance, may be negative but value of variances should always positive. But sometimes these values become negative; there are three possible reasons for occurrence of negative variances:
a. Error in sampling:
If sampling size is too small or anomalous sapling can possibly a cause of sampling error. Sometimes there are unusual values in data that are result of either scaling error or human error, occur while data collection effect precision of estimates. These values maximize error variance which then adversely affects other estimates. Caution must be adopted to avoid these aberrant values as much as possible.

b. Genotype  Environment Interaction:
Presence of this interaction may enlarge some component of variance like error variance. Large error variance affects other component of variances which are obtained by subtracting error