成年晋南牛体重估测公式的探讨

<正> 目前,农村和部分牛场因缺少地磅设备,直接称重较为困难。因此,寻找准确而简易地估测晋南牛体重的方法,有着重要的现实意义。 多年来,晋南牛在选育中一直沿用四十年代美国学者约翰逊推导的估重公式。(体重=体长×胸围~2÷10800)进行估重。为了验证公式对晋南牛的实用价值,作者在晋南牛重点产区的运城县和河津县分别实测了76头中等膘情成年母牛(空怀或怀孕3月以内)和55头中等膘情成年犍牛体尺和体重资     

牛基本数据的测定方法

<正> 1 体重牛的体重,以实测重最为准确。但由于广大农村没有地磅,实测体重有一定困难,可以采用估测法来估测体重。体重估测的公式很多,由于牛的品种不同,存在着个体差异。一般认为,估重与实际重相差在5%以内,则认为效果良好,如果超过5%则不能应用。在实际工作中,可以事先进行校正,甚至对公式中的常数做必要的修正,以求更准确。各类黄牛常用估重公式如下,黄牛体重(千克)=[胸围(厘米)]~2×体斜长(厘米)/10 800。2 膘情等级牛的膘情直接关系到产肉性能。评膘也叫肥度鉴过评膘,有丰富实践经验的人可以直接估计出牛     

如何估算家畜体重

为方便对家畜按体重给药和估算出肉率及市场牲畜交易估重,现将传统及实践中积累改进的部分家畜体重估算公式收集如下: 1.马骡驴体重=胸围×体高×0.016 注:体高指正常站立时由髻甲顶端到地面垂直高度。 母马体重=(胸围~2×斜体长+25)÷10800 注:此式适用3周岁以上母马,如不满3周岁应将25     

应用二元回归原理推导渤海黑牛体重估测公式

本文阐述了以30头3岁及3岁以上空怀或怀孕6个月以前中等膘情母牛体重为依变量y,以体斜长x_1、胸围x_2为自变量,利用二元回归原理,对渤海黑牛体重估测公式进行了推导。通过F检验(P>0.01)、复相关系数检验(P<0.01)和实践检验,体重估测值与实际平均值仅差2.3kg,每头误差绝对值平均11.3kg,误差率3.26％(P>0.05);较约翰逊估重公式所得6.88％误差率降低3.62％,说明所得回归方程y=2.14x_1+3.37x_2-522.09较客观地反映了上述母牛体重的变化规律。然后,对相同年龄范围怀孕8个月以上中等膘情母牛空怀或怀孕6个月以前的上等,下等膘情母牛及公、阉牛的体重估测公式进行了校正。为便于记忆和实际应用,对所得公式进行了简化。     

如何提高奶牛受胎率

<正>1搞好奶牛的受配率1.1育成牛可在体成熟初期15~16个月龄,体重达360千克(体重估测=胸围2(米)×体斜长(米)×87.5,发情即可适时配种。     

沿江牛成年母牛体重与体尺指标的相关与回归分析

以辽宁省宽甸县沿江牛保种区2004年测量的138条相关数据为基础,分析了体重与年龄、体长、体高、胸围、管围的相关系数,同时,进一步分析了估测沿江牛成年母牛体重的回归模型。结果表明:体重与体高、体长、胸围、管围、年龄分别为0.611、0.661、0.888、0.632、0.290;得到了2个估测体重的回归模型,估测值与实测值之间的相关程度分别为0.933和0.928。     

青海互助八眉猪体重与体尺多元回归分析

为了探究青海互助八眉猪体尺指标对体重的影响,本实验选取原种八眉猪88头,其中公猪17头,母猪71头,测量了体重(Y)、体高(X₁)、体长(X₂)、胸围(X₃)、背高(X₄)、胸深(X₅)、腹围(X₆)、管围(X₇)和背膘厚(X₈)等指标,通过相关系数分析、通径分析和逐步回归分析,来评价各体尺指标对八眉猪体重的影响。结果表明,八眉猪公猪和母猪体尺指标与体重相关性较大的均为胸围,分别是r公=0.97和r母=0.91。八眉猪公猪胸围和腹围对体重直接影响较大,体高、体长、背高、胸深、管围和背膘厚对体重间接影响较大;八眉猪母猪体长、胸围、胸深、腹围、管围和背膘厚对体重直接影响较大,体高和背高对体重间接影响较大。公猪最优回归方程为Y=-184.02+1.76X₃+0.74X₆,母猪的最优回归方程为Y=-179.46+0.33X₂+0.34X₃     

无角陶赛特羊在甘肃河西走廊地区杂交效果的研究

用观察、试验和综合分析等方法,对甘肃省永昌肉用种羊场引自新西兰的无角陶赛特品种羊的杂交效果进行了研究,探讨了无角陶赛特羊在甘肃河西走廊地区与土种羊的杂交效果.研究结果表明①无角陶赛特公羊和土种母羊的F1外貌与无角陶赛特羊更为相似;F1公羔初生重5.08kg±0.94 kg,周岁重46.92kg±4.59 kg;F1母羔初生重5.55 kg±0.70 kg,周岁重43.45kg±4.93 kg.F1各年龄阶段体重与土种羊相比,提高效果极显著(P＜0.01);②可通过回归方程预测F1的体重体重(kg)=0.126×体长(cm)+0.290×体高(cm)+0.473×胸围(cm)-26.539(r=0.879 2).     

应用二元回归原理推导渤海黑牛体重估测公式

本文阐述了以30头3岁及3岁以上空怀或怀孕6个月以前中等膘情母牛体重为依变量 y,以体斜长 x_1、胸围 x_2为自变量,利用二元回归原理,对渤海黑牛体重估测公式进行了推导。通过 F 检验(P<0.01)、复相关系数检验(P<0.01)和实践检验,体重估测值与实际平均值仅差2.3千克,每头误差绝对值平均11.03千克,误差率3.26%(P>0.05);较约翰逊估重公式所得6.88%误差率降低3.62%,说明所得回归方程=2.14x_1+3.37x_2-522.09较客观地反映了上述母牛体重的变化规律。然后,对相同年龄范围怀孕6个月以上中等膘情母牛,空怀或怀孕6个月以前的上等、下等膘情母牛及公、阉牛的体重估测公式进行了校正。为便于记忆和实际应用,对所得公式进行了简化。     

张掖肉牛生长发育规律研究

【目的】本文从生产性能测定、生长曲线绘制入手,探讨张掖肉牛体重与各体尺性状与之间的相关性,构建通过体尺预测体重的回归方程,为种群扩繁和高效推广利用提供科学依据 。【方法】选择核心育种场公、母牛,从出生连续测定7个阶段的体重、体高、十字高、体斜长、胸围和腹围至3周岁,将测得数据首先用Excel 2010进行初步整理,计算绝对生长和相对生长值,绘制相应的曲线。然后,利用 IBM SPSS Statistics 19进行处理,pearson相关系数计算法做零阶相关和偏相关性分析,采用逐步法设计回归模型,并进行显著性和拟合度检验。【结果】确定了 Y4=-637.950+3.286× x4+2.282 ×x3+0.243 ×x5+0.748 ×x1,Y5=-881.017+4.057× x4+2.836× x3+0.407 ×x5+1.066× x1,为张掖肉牛最佳线性回归方程,拟合度较、高误差较小,可以用于生产预测。【结论】张掖肉牛体重和胸围、体斜长、腹围、体高4个变量呈极显著正相关关系,当其他变量不变时,胸围、体斜长、腹围或体高每增加1cm,母牛体重增加3.286 kg、2.282 kg、0.243kg和0.748kg,公牛体重增加4.057 kg、2.836 kg、0.0.407kg和1.066kg。     

Application of real-time ultrasound technology to estimate in vivo breast muscle weight of broiler chickens

1. Real-time ultrasound (RTU) is a fast, non-destructive and relatively inexpensive technique to estimate body composition in animals. 2. A total of 835 Hubbard, Ross and Cobb broilers from different flocks were randomly selected, weighed and two RTU measurements were taken from both sides of their breast muscles (BM). Immediately following ultrasonography, broilers were processed and dissected to determine carcase, boneless BM, leg quarter and wing weights. Data were utilised to develop multiple linear regression equations (MLRE) to estimate carcase part weights. 3. Factors such as sex, age or genetic line did not contribute significantly to the accuracy of the models. The measurement in the right side was consistently more efficient than the left for estimating BM weight. 4. The following MLRE was estimated from live body weight (BW) and RTU area images: BM (g) = -94.3476 + 0.1518 * BW (g) + 5.1644 * BM-RTU area (cm2) (R2 = 0.97). 5. Due to the allometric relationships among body parts the following equations were also estimated: Legs (g) = -56.6738 + 0.2846 * BW (g) + 2.1570 * BM-RTU area (cm2) (R2 = 0.98) and Total Meat Cuts (g) = -142.0567 + 0.4638 * BW (g) + 5.1236 * BM-RTU area (cm2) (R2 = 0.99). 6. The results indicated that it was possible to estimate BM and other carcase cut weights with high accuracy from RTU measurements.     

Relationship of body measurements and body condition score to body weight in modern Finnish Ayrshire cows

Abstract

The differences in body weight (BW) and measurements between the cows of today and the early 70s was evaluated and the usefulness of heart girth (HG), wither height, body length (BL) and body condition score (BCS) as predictors of the BW of modern Finnish Ayrshire cows was estimated. During the last three decades, the BW, HG and BL has increased indicating change in body conformation. The BW prediction equation based on early 70s data underestimate the BW of modern Ayrshire cows. Based on current data it is recommended to use different models to predict BW for primiparous and multiparous cows. From single measurements, HG predicted BW most accurately. Inclusion of BL in model gave slight improve in BW prediction, especially for primiparous cows. An additional term of age or days in milk (DIM) for primiparous cows and BCS or DIM for multiparous cows along with HG increased slightly the accuracy of BW prediction.     

Growth Rates in Thoroughbred Horses Raised in Florida

SummaryGrowth rates of thoroughbred horses are not as well defined as those of other farm animals, and only a few articles summarize growth of thoroughbred horses over a prolonged period. Body weight (BW), heart girth (HG), wither height (WH), body length (BL), and hip height (HH) of 128 thoroughbred horses (59 colts and 69 fillies) were recorded from birth to 15 months of age at 14- or 28-day intervals. Data were obtained from consecutive 20 foal crops. At birth (0 day), BW was 53.55 ± 5.20 kg (range, 39.04–67.19), HG was 0.82 ± 0.03 m (range, 0.75–0.90), WH was 1.02 ± 0.03 m (range, 0.93–1.10), BL was 0.74 ± 0.03 m (range, 0.67–0.82), and HH was 1.05 ± 0.03 m (range, 0.93–1.14). At weaning (112 ± 3 days), BW was 199.57 ± 13.58 kg (range, 163.44–234.26), HG was 1.29 ± 0.04 m (range, 1.19–1.37), WH was 1.27 ± 0.03 m (range, 1.21–1.35), BL was 1.17 ± 0.03 m (range, 1.08–1.30), and HH was 1.32 ± 0.03 m (range, 1.24–1.39). At 6 months (181 ± 4 days), BW was 237.16 ± 18.48 kg (range, 186.14–288.74), HG was 1.36 ± 0.04 m (range, 1.24–1.45), WH was 1.33 ± 0.03 m (range, 1.26–1.40), BL was 1.25 ± 0.03 m (range, 1.17–1.33), and HH was 1.38 ± 0.03 m (range, 1.28–1.44). At 12 months (361 ± 8 days), BW was 337.73 ± 26.61 kg (range, 267.86–394.98), HG was 1.56 ± 0.05 m (range, 1.41–1.66), WH was 1.45 ± 0.03 m (range, 1.36–1.55), BL was 1.42 ± 0.04 m (range, 1.31–1.51), and HH was 1.49 ± 0.03 m (range, 1.41–1.57). At 15 months (447 ± 8 days), BW was 392.48 ± 30.61 kg (range, 317.80–457.18), HG was 1.64 ± 0.05 m (range, 1.52–1.76), WH was 1.49 ± 0.03 m (range, 1.42–1.58), BL was 1.48 ± 0.04 m (range, 1.40–1.59), and HH was 1.53 ± 0.03 m (range, 1.46–1.62). Two regression equations (y₁ from birth to 112 days of age and y₂ from 113 to 450 days of age) were calculated. WT_kg is estimated by y₁ = 1.28x + 57.82 (R² = 0.94) and y₂ = 0.57x + 133.28 (R² = 0.86). HG_m is estimated by y_l = 0.0041x + 0.86 (R² = 0.90) and y₂ = 0.0011x + 1.16 (R² = 0.84). WH_m is estimated by y₁ = 0.0022x + 1.03 (R² = 0.85) and y₂ = 0.0006x + 1.22 (R² = 0.80). BL_m is estimated by y₁ = 0.0038x + 0.77 (R² = 0.92) and y₂ = 0.0009x + 1.09 (R² = 0.85). HH_m is estimated by y₁ = 0.0024x + 1.07 (R² = 0.87) and y₂ = 0.0006x + 1.27 (R² = 0.78).     

Model for predicting egg output and metabolisable energy intake of laying pullets.

1. The data compiled by Marsden and Morris (1987) to examine the relationships between environmental temperature and the long-term, adapted responses of laying pullets were divided at random into two subsets of 99 and 113 observations. The first subset was used to estimate regression coefficients for an econometric model, and the second subset to validate the model. 2. Equations to predict inputs (costs) and outputs (returns) were estimated with a three-stage least-squares regression model. Three stage least-squares estimation is a technique which corrects for the simultaneity of variables within the model and correlation across equations of the model. This results in more efficient estimates of the regression coefficients. 3. The final output and output equations were: MEI = 253.86-190.31EM+5.766EM2-0.546EM3 + 0.7034T-0.004388T3 + 695.08BW-120.23BW2 + 397.37ME-13.132ME2-1.06MEXT; R2 = 0.86; EO = 119 + 0.025MEI -0.0000045MEI2-1.462T-0.0791T2-135.3BW + 38.31BW2-1.483T X BW + 0.0288T2 X BW + 0.673 delta BW; R2 = 0.59 where MEI = daily metabolisable energy intake (kJ/bird d), T = environmental temperature (degree C), EO = egg output (g/bird d), BW = body weight, and ME = metabolisable energy concentration (kJ/g). The values for R2 indicate very good fits considering that the data were recorded over a 26-year period in 14 different laboratories. 4. This statistical model can serve as the basis for an econometric model of egg production to determine the environmental temperature that maximises profits from laying pullets of different body weights.     

基因芯片技术在晋南牛种公牛选育中的应用

为了更好的保护开发利用晋南牛,确保晋南牛的遗传多样性,本研究应用基因芯片技术,对晋南牛进行群体遗传特性的检测及后备种公牛的遗传评估,为晋南牛的分子辅助选育与保种提供理论与技术支持。采集18月龄健康、体重相近（（350±20）kg）的荷斯坦牛、和顺肉牛、西门塔尔牛、延边牛及利木赞牛血样各10份,及晋南牛后备公牛血样25份,根据不同牛品种分为6组,其中前5组每组10个重复,晋南牛后备公牛25个重复。应用Illumina SNP 50K高密度牛SNP芯片进行基因型检测,分析比较晋南牛的群体遗传特征,运用亲缘矩阵计算晋南牛后备公牛的亲缘系数,同时用BLUP进行遗传评估。结果表明,晋南牛在遗传结构上与荷斯坦牛、和顺肉牛、西门塔尔牛及利木赞牛关系较远,与延边牛较近,为中国地方品种群体;对晋南牛后备公牛进行遗传评估,得出了牛的基因组胴体重方差育种值排名,JN23的胴体重倍数性状标准差最大,从基因组水平可选作肉用种公牛;应用亲缘分析对晋南牛后备公牛家系进行分类,避免群体间的近交。本研究对晋南牛后备公牛进行了遗传评估、近交家系分析、传统表型选择及遗传疾病检测,最终选留的种公牛为JN07、JN23、JN05、JN08、JN02、JN13、JN19、JN14,通过多种选育方法结合提高了公牛的选择准确性,为晋南牛的群体选育提高奠定了基础。     

南阳牛生长性状相关基因组区域全基因组关联分析

本研究旨在筛选和鉴定与南阳牛生长性状相关的基因组区域和候选基因,从而更好地了解牛生长性状的遗传机制。试验共采集71头南阳牛母牛血样并提取基因组DNA,利用SLAF-seq（specific-locus amplified fragment sequencing）技术获得全基因组SNP标记并对试验个体基因型进行分型。对每头个体初生重及不同月龄（6、12、18、24、36）的体重、体高、体斜长、胸围和坐骨端宽及每6个月体增重等生长性状进行全基因组关联分析;获得显著相关的基因组区域后,对其中基因进行功能注释以筛选候选基因。结果显示,共获得141 755个筛选后的SNPs,通过全基因组关联分析鉴定出5个分别与12月龄体重（8号染色体：17 320 634~17 347 720 bp）、12月龄胸围（2号染色体：15 063 190~15 155 309 bp）、24月龄体斜长（11号染色体：60 727 342~81 425 987 bp）、36月龄坐骨端宽（14号染色体：15 635 762~15 643 272 bp）和12~18月龄体增重（26号染色体：40 456 192~40 456 477 bp）等生长性状显著相关的基因组区域（LOD≥6.35）。通过对5个基因组区域内的186个基因进行功能注释,共筛选得到11号染色体上的8个基因（BMP10、IFT172、SDC1、TCF23、TRIM54、RAB1A、VPS54和GDF7）与骨生长、肌肉发育和生长调控有关,建议其可优先作为牛生长性状相关候选基因进行进一步验证。     

Determination of saleable product in finished cattle and beef carcasses utilizing bioelectrical impedance technology

Two experiments were performed to develop prediction equations of saleable beef and to validate the prediction equations. In Exp. 1, 50 beef cattle were finished to typical slaughter weights, and multiple linear regression equations were developed to predict kilograms of trimmed boneless, retail product of live cattle, and hot and cold carcasses. A four-terminal bioelectrical impedance analyzer (BIA) was used to measure resistance (Rs) and reactance (Xc) on each animal and processed carcass. The IMPS cuts plus trim were weighed and recorded. Distance between detector terminals (Lg) and carcass temperature (Tp) at time of BIA readings were recorded. Other variables included live weight (BW), hot carcass weight (HCW), cold carcass weight (CCW), and volume (Lg2/Rs). Regression equations for predicting kilograms of saleable product were [11.87 + (.409 x BW) - (.335 x Lg) + (.0518 x volume)] for live (R2 = .80); [-58.83 + (.589 x HCW) - (.846 x Rs) + (1.152 x Xc) + (.142 x Lg) + (2.608 x Tp)] for hot carcass (R2 = .95); and [32.15 + (.633 x CCW) + (.33 x Xc) - (.83 x Lg) + (.677 x volume)] for cold carcass (R2 = .93). In Exp. 2, 27 beef cattle were finished in a manner similar to Exp. 1, and the prediction equations from Exp. 1 were used to predict the saleable product of these animals. The Pearson correlations between actual saleable product and the predictions based on live and cold carcass data were .91 and .95, respectively. The Spearman and Kendall rank correlations were .95 and .83, respectively, for the cold carcass data. These results provide a practical application of bioelectrical impedance for market-based pricing. They complement previous studies that assessed fat-free mass.     

Relationship between latency of brainstem auditory-evoked potentials and head size in dogs.

Cranium and brainstem dimensions were measured in 32 postmortem dog heads. Positive correlations were found between cranium length (CL) and brainstem length (BL) (r = 0.87), between cranium width (CW) and brainstem width (BW) (r = 0.83), and between cranium distance (CD = CL+CW/2) and brainstem distance (BD = BL+BW/2) (r = 0.91). Positive correlation coefficients were also found between CL and CW (r = 0.90), and between BL and BW (r = 0.85). It was concluded that head size accurately reflected brainstem size. A least squares estimation of the brainstem distance (BD) from CL and CW values was BD = 10.9 + 0.16 (CL+CW/2) (BD, CL and CW in mm). Brainstem auditory evoked potentials (BAEPs) and cranium dimensions were measured in 43 dogs (86 ears) with different head size, body size, sex and age. Wave form, absolute and interpeak latencies and correlation coefficients, relating latencies to cranium dimensions and body weight, were analysed. CL, CW, and CD were positively correlated with body weight (r = 0.93, 0.70 and 0.93, respectively), and CL, CW, and CD were correlated with age (r = 0.33, 0.52, and 0.40, respectively). BAEPs consisted of five distinct positive peaks (I to V). Secondary positive peaks following peaks I and II were seen in 60% (I') and 90% (II') of the recordings. Late waves were recorded in 90% (VI), 50% (VII), and 25% (VIII) of the recordings. Latencies increased with decreasing stimulus intensity level (from 90 dB to 10 dB hearing level, HL), especially for peaks I, II, V, and the I-V interpeak interval.(ABSTRACT TRUNCATED AT 250 WORDS)     

Genetic parameter estimation for pre- and post-weaning traits in Brahman cattle in Brazil

Beef cattle producers in Brazil use body weight traits as breeding program selection criteria due to their great economic importance. The objectives of this study were to evaluate different animal models, estimate genetic parameters, and define the most fitting model for Brahman cattle body weight standardized at 120 (BW120), 210 (BW210), 365 (BW365), 450 (BW450), and 550 (BW550) days of age. To estimate genetic parameters, single-, two-, and multi-trait analyses were performed using the animal model. The likelihood ratio test was verified between all models. For BW120 and BW210, additive direct genetic, maternal genetic, maternal permanent environment, and residual effects were considered, while for BW365 and BW450, additive direct genetic, maternal genetic, and residual effects were considered. Finally, for BW550, additive direct genetic and residual effects were considered. Estimates of direct heritability for BW120 were similar in all analyses; however, for the other traits, multi-trait analysis resulted in higher estimates. The maternal heritability and proportion of maternal permanent environmental variance to total variance were minimal in multi-trait analyses. Genetic, environmental, and phenotypic correlations were of high magnitude between all traits. Multi-trait analyses would aid in the parameter estimation for body weight at older ages because they are usually affected by a lower number of animals with phenotypic information due to culling and mortality.     

Body measurements of west African dwarf sheep as parameters for estimation of live weight

The relationships between live weight and eight body measurements of West African Dwarf (WAD) sheep were studied using 210 animals under on farm condition. Data obtained on height at withers (HW), heart girth (HG), body length (BL), head length (HL), head width (HDW), loin girth (LG), length of hindquarter (LHQ) and width of hindquarter (WHQ) were fitted into linear, allometric and multiple regression models to predict live weight from the body measurements. Results revealed that body measurements of WAD sheep were generally higher in the rams than in the ewes. Coefficient of determination (R(2)) values computed for the body measurements were generally higher (0.87-0.99) using allometric regression model than linear regression model (0.44-0.94). Heart girth (HG) and WHQ depicted the highest relationship to live weight in linear and allometric models compared to other body measurements. Based on stepwise elimination procedure, HG, HL and WHQ were better in predicting live weight in multiple linear regression models. The magnitude of correlation coefficient (r) indicate that WHQ shows the highest correlation with live weight (r = 0.96) compared to HG (r = 0.94).