Variation Law Of Blood Gas Indexes, Segmentation Speed And Gait Characteristics Of Ili Horse In Trotting Race

This article mainly studied the variation law of blood gas indexes, segmentation speed，gaitcharacteristics, body angles of Ili horse which is from Zhaosu Stud in1000m trot training race.Then differences of these indexes, correlation between these indexes and speed were analyzed.Linear fitting between segmentation speed and full speed and nonlinear regression between bodyangle and movement processes were carried out. Correlation between Mathematical modelparameters and speed was analyzed by partial correlation analysis. The result is as follow:1. By comparing with blood gas indexes of lli horses in the1000m trot training game beforeand after exercise, the content of PvCO₂, PvO₂, HCO₃^-, TCO₂, SvO2and Glu in the venous blooddid not change significantly （P>0.05）. Blood pH, BE and Ca²⁺were significantly lowerimmediately after the game （P<0.05）, but after15min rest basic rebounded to motionless level. K⁺,Hct and Hb immediately after the game were significantly increased （P<0.05）, but after15minrest basic back down to motionless levels. Blood pH value was highly significant positivecorrelation with BE and HCO-3（P<0.01）, and significantly positive correlation with the TCO₂concentration （P<0.05）, Hct, Hb were highly significant negative correlation with pH,PvCO₂,HCO3（-P<0.01），Hct, Hb were highly significant positive correlation with K⁺（P<0.01）.The results suggest that: There are shrink and blood concentration phenomenon of the Ili horsesbody in1000m trotting. BE, HCO-3, TCO₂, Hct and Hb on acid-base balance all have certainadjustment. Na⁺, K⁺and Ca²⁺on the body movement have certain adjustment.2. The race characteristic of each section speed was analyzed in1000m trot training game,there was a certain difference between each section speed, and it can effectively improve the speedof the entire race that properly adjusting the fastest section speed in the second half. The simplecorrelation analysis showed that it is the fundamental guarantee to win1000m trot training gamethat maintains a higher speed at midway straight. If the specific performances of horses are veryclose, it often can win the competition by improving the final sprint ability. This explains that thesecond limit accelerate ability is very important for1000m trotting race. In the training process,according to the segmentation speed characteristics and the regression model, the trainer and ridercan effectively improve the achievements of horse by arranging every50m section speedreasonably. Two dynamic parameters between stride frequency and length are interdependent, andone parameter changes will cause the changes of another parameter. Horse will get the bestachievement while the two parameters achieve to the optimum combination. In the1000m trottraining game, the stride frequency and length are related or significant related to the speedparameters （P <0.05or P <0.01） in the straights and corners. It needs to appropriately increase thestride length while maintaining stride frequency in the straight line. However, the stride frequencyis more important than stride length in the corners, and it can improve the speed by increase stridefrequency at this stage. In the corner, the height of the back foot should be increased in the trainingprocess which is the main factors affecting the speed, but, the height of the front foot is veryimportant to increase speed （P <0.05） in the straight line. Body height and height at withers ishighly significantly positive correlation with the average speed （P<0.01）. The rate of heart girthand soma index （heart girth/height at withers） are negative correlation with the average speed, andthe soma index （heart girth/height at withers） is significant negative correlation with the speed（P<0.05）. The first test speed is significantly lower than the second test （P<0.05）, and verysignificantly lower than the third test results （P<0.01） in the Ili horse training game. Over trainingtime, most of correlation coefficients between segmentation speed and full speed increase, and thesegmentations which is related or significant related become more. This result indicates that thesegmentation which has maximum correlation coefficient appearance more early, then horses canbe in longer period of time to maintain the high-speed movement, thereby effectively improving the speed of the whole schedule.3. Though studying the body angle of Ili horse in the trot training, found that there is a bigstretch among the shoulder angle, the front fellock angle, the front fetlock angle, hip joint angle,stifle angle, the back fellock angle and the back fetlock angle. Discrete degree of the absolutevalue is also big. However, the stability of the rest of the angle is better. The stretch of the frontfetlock angle and stifle angle, the maximum extension of stifle angle are correlation with the speed.Meanwhile, there is a significant positive correlation between the stretch of the front fetlock angleand the speed, and there is a very significant positive correlation between the stretch of the frontstifle angle, the maximum extension of stifle angle and the speed, By improving the degree ofstretching of the front fetlock angle and stifle angle can effectively improve the speed of lli horses.The changes of stifle angle and the back fellock angle reflect the horses force and buffer capacityin the trotting. It can effectively ensure the effective power of horses and horse health by moderateadjustment.7angle mathematical models can be built though change of the shoulder angle, thefront fellock angle, the front fetlock angle, hip joint angle, stifle angle, the back fellock angle andthe back fetlock angle with the passage of time, then partial correlation analysis is carried out onthe function parameters and speed. The result:（1）The fitting equation of the shoulder angle:f（x）=a1*exp（-（（x-b1）/c1）^2）+a2*exp（-（（x-b2）/c2）^2），the range of parameters: a1（85.58¹66.4）,b1（-0.111⁰.894）,c1（0.1226⁰.637），a2（85.26¹91.5），b2（-0.01256¹.228），c2（0.1259⁰.9603）,where the parameter b2highly significant negative correlation with speed （P<0.01）;（2）The fittingequation of the front fellock angle: f（x）=a1*exp（-（（x-b1）/c1）^2）+a2*exp（-（（x-b2）/c2）^2），therange of parameters:a1（163.6²25.7）,b1（-0.07799⁰.5377）,c1（0.1108⁰.3692）,a2（134¹95.7）,b2（-0.02651⁰.554）, c2（0.08363⁰.3347）,where there was a significant positive correlationbetween the parameter a1and speed （P<0.05）;（3）The fitting equation of the front fetlock angle:f（x）=a0+a1*cos（x*w）+b1*sin（x*w），the range of parameters: a0（171.5²15）,a1（-9.664⁵0.35），b1（-65.43^-16.74）, w（7.585¹2.1）,in which parameter a0and speed, there were significantpositive correlation （P<0.05）, there was a significant negative correlation between the b1andspeed （P<0.05）;（4） The fitting equation of hip joint angle: f（x）=a0+a1*cos（x*w）+b1*sin（x*w），the range of parameters: a0（56.27⁸2.97）, a1（-8.13¹6.3）, b1（-17.66⁴.778）,w（10.51²1.56）,inwhich parameters a1,b1and speed, there were significant positive correlation, between a0andspeed（P<0.05）, there was a significant negative correlation（P<0.05）;（5） The fitting equation ofstifle angle: f（x）=a0+a1*cos（x*w）+b1*sin（x*w）+a2*cos （2*x*w）+b2*sin （2*x*w），the range ofparameters: a0（-112.2^-86.04）,a1（-16.11²5.32）,b1（-24.24¹0.84）,a2（-6.337¹2.27）,b2（-11.83⁹.804）,w（9.599¹5.29）, where the parameter a0highly significant positive correlation with speed（P<0.01）, between b1, w and speed there were significant positive correlation （P<0.05）, there wasa significant negative correlation between the a1and speed （P<0.05）;（6） The fitting equation ofthe back fellock angle:f（x）=a1*exp（-（（x-b1）/c1）^2）+a2*exp（-（（x-b2）/c2）^2）+a3*exp（-（（x-b3）/c3）^2）, the range of parameters: a1（50.32¹60）,b1（0.3034⁰.9274）,c1（0.07915⁰.573）,a2（69.52¹45.8）, b2（-0.1911⁰.2057）, c2（0.1024⁰.5043）,a3（38.24³60.4）,b3（-0.201¹.035）,c3（0.07889⁰.3748）, which exists between the parameters b1, c2, and speed significantly negativelycorrelated （P<0.05）;（7） The fitting equation of the back fetlock angle f（x）=a1*exp（-（（x-b1）/c1）^2）+a2*exp（-（（x-b2）/c2）^2）+a3*exp（-（（x-b3）/c3）^2），the range of parameters: a1（50.32¹60）,b1（0.3034⁰.9274）,c1（0.07915⁰.573）,a2（69.52¹45.8）,b2（-0.1911⁰.2057）,c2（0.1024⁰.5043）,a3（38.24³60.4）, b3（-0.201¹.035）,c3（0.07889⁰.3748）, there is no correlation between allparameters and speed. From the fit of the mathematical model and the correlation betweenparameters and speed found that Gaussian fitting reached a high degree, and Fourier fitting iscloser to the actual situation of the trotting race.