A Mathematical Model for Assessing Fertilization Ability of Landrace Boar and Its Establishment Method
A technology of fertilization ability and mathematical model, which is applied in the fields of electrical digital data processing, special data processing applications, instruments, etc., can solve the problem of inability to accurately assess the fertilization ability of boars, and achieve the effect of narrowing the gradient change interval.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Publication Date
- 2018-04-10
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Abstract
Description
technical field
[0001] The invention belongs to the technical field of animal genetics, breeding and reproduction. More specifically, the invention relates to a mathematical model for evaluating the fertilization ability of Landrace boars and a method for establishing the same. Background technique
[0002] Production indicators such as farrowing rate, litter size, litter live piglets, and piglets per parity contribution of participating sows are the ultimate standards to measure the fertilization ability of boars, and the long-term actual pig production mainly depends on Boar semen quality was assessed. In production, the quality of boar semen is mainly judged by visual inspection and microscopic inspection. Among them, the naked eye inspection mainly observes the ejaculate volume, semen color, smell, pH and other indicators. The single ejaculate volume of boars is 200-300mL, which is off-white or milky white and has a special fishy smell. The pH value is 6.9-7.5; It main...
Examples
Embodiment 1
[0039] Embodiment 1 A method for establishing a mathematical model of Landrace boar's fertilization ability
[0040] see figure 1 , is the technical roadmap for establishing the mathematical model of the Landrace boar's fertilization ability in this embodiment, and the method for establishing the mathematical model of the Landrace boar's fertilization ability in the present embodiment includes the following specific steps:
[0041] 1) Selection of predictor variables
[0042] 18 variables that can reflect the fertilization ability of boars were selected as the predictive variables of the analysis model, including farrowing rate (FR), litter size (LS), litter size (Number of bornalive (NBA), Number of dead piglets (NDP), Healthy piglets (Number of qualified piglets, NQP), Number of abnormal piglets , NAP), Number of stillborn piglets (NSP), Number of weak piglets (NWP), Number of mummified piglets (NMP) ), piglet birth weight (Average piglet weight, APW), number of piglets p...