Rumen volatile fatty acid prediction method, system, equipment and medium

A technology of volatile fatty acids and prediction methods, which is applied in health index calculation, medical informatics, informatics, etc., can solve the problems of lagging adjustment of feeding management measures, high test intensity, and high input of manpower and material resources, so as to avoid the risk of invalid tests, The effect of improving research efficiency

Pending Publication Date: 2021-02-26

NORTHWEST A & F UNIV

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AI-Extracted Technical Summary

Problems solved by technology

Appropriate VFA is beneficial to the development of rumen epithelial cells, but excessive VFA accumulation in the rumen can easily cause a decrease in rumen pH and cause rumen acidosis

[0006] In ruminant nutrition research, there are problems such as high manpower and material resources input, high test intensity, large individual differences, and lagging adjustment of feeding management measures. The i...

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Method used

In another specific embodiment of the present invention, described data set is processed through standardization before modeling, and matches with ration information, animal information, helps to reduce the workload of predictive modeling, pr...

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Abstract

The invention provides a rumen volatile fatty acid prediction method, system and equipment and a medium, and belongs to the technical field of nutrition regulation and prediction. By analyzing existing data, a rumen volatile fatty acid prediction model is constructed, and then rumen fermentation is predicted according to daily ration information and animal information. The optimal prediction modelis obtained through screening, the ruminant nutrition law is effectively disclosed, and the ruminant nutrition test result is predicted. The method and system play a pre-test role to a certain extent, effectively avoid invalid test risks before the test is carried out, also can provide reference in the test period to timely adjust feeding management, and improve the research efficiency, so that the method and system have a good practical application value.

Application Domain

Health-index calculationNutrition control

Technology Topic

Ruminal fermentationNutrition +9

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Examples

Experimental program(6)

Example Embodiment

[0054]Example 1

[0055]Literature search and data screening

[0056]The literature search uses databases such as China HowNet to screen relevant literature data for the past 10 years. The acceptance criteria are: the experimental animals are dairy cows, beef cattle, sheep and goats; the literature reports the composition and nutritional components of the diet (additives, RDS, peNDF, etc.); the experimental indicators are the rumen fermentation parameters (pH, TVFA content, molar ratio of VFA, propylene acetate Acid ratio). The RDS in the diet is based on the reported value in the literature. If the data is not reported in the article, use the CPM-Dairy software to estimate these parameters. A total of 55 articles and 78 sets of valid data were obtained.

Example Embodiment

[0057]Example 2

[0058]Stepwise regression analysis modeling

[0059]Using stepwise regression analysis, stepwise regression calculations were performed on the 9 indicators of diet formula and animal information and the corresponding 10 rumen fermentation parameter indicators, and the equations and determination coefficients related to 10 rumen fermentation parameter indicators were obtained respectively (Table 1). R2max=0.50, which does not meet the modeling requirements.

[0060]Table 1 Coefficients of independent variables and coefficients of determination of the stepwise regression model

[0061]

Example Embodiment

[0062]Example 3

[0063]Partial Least Squares Regression Analysis Modeling

[0064]Partial least squares regression analysis was used to perform regression operations on the 9 indicators of diet formula and animal information and the corresponding 10 rumen fermentation parameter indicators, and the equations and determination coefficients related to the 10 rumen fermentation parameter indicators were obtained respectively (Table 2) , Where R2max=0.54, which does not meet the modeling requirements.

[0065]Table 2 Partial Least Squares Regression Model Independent Variable Coefficients and Coefficient of Determination

[0066]

[0067]

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Description & Claims & Application Information

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Rumen volatile fatty acid prediction method, system, equipment and medium

AI-Extracted Technical Summary

Problems solved by technology

Method used

Abstract

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Example Embodiment

Example Embodiment

Example Embodiment

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Description & Claims & Application Information

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