我国牛肉产量级制度建立的借鉴与思考

完善的牛肉分级体系一般都包括质量等级标准和产量等级标堆。产量等级主要描述胴体的产肉性能，如产肉率等，与胴体重、背膘厚，眼肌面积以及胴体的厚度等指标密切相关。文中详知介绍各国产量等级标准，对我国产量等级标准的制订提出一些建议。     

国内外肉类加工技术的现状和发展趋势（下）

2.3胴体智能化分级技术美国的牛肉分级标准包括牛肉质量等级标准和牛胴体产量等级标准两部分。牛肉的质量等级由美国农业部指派的独立牛肉评定员通过综合上述两个等级值来判定,并使用不同的等级标志。本着自愿、付费、分级员评定三原则,美国推行牛肉分级标准取得了明显的效果,牛肉分级率由初期的0．5％发展到目前的95％,尽管肉牛品种由5个增加到100个,     

牛肉的分级技术

牛肉等级评定包括胴体质量等级评定和产量等级评定。胴体质量等级评定是在牛胴体冷却后,对12～13胸肋间眼肌切面处的大理石花纹、肉色和脂肪色(在强度为660勒克斯的光线下)等指标以及生理成熟度进行评定,主要按大理石花纹级别和生理成熟度级别将牛胴体分为特级、优一级、优二级     

国内外肉类加工技术的现状和发展趋势(下)

正(接上期)2.3胴体智能化分级技术美国的牛肉分级标准包括牛肉质量等级标准和牛胴体产量等级标准两部分。牛肉的质量等级由美国农业部指派的独立牛肉评定员通过综合上述两个等级值来判定,并使用不同的等级标志。本着自愿、付费、分级员评定三原则,美国推行牛肉分级标准取得了明显的效果,牛肉分级率由初期的0.5%发展到目前的95%,尽管肉牛品种由5个增加到100个,遗传变异增大,但牛肉质量由遍布8级到集中在     

等级和产量是胴体质量的可靠度量吗？（上）

等级和产量是不是猪肉胴体质量和价值的可靠度量,这基本上是两个方面的问题。对此问题的答案通常是“是”或“否”。但这些答案并非都是那么简单的。在实际上,等级和产量的精确度,对于按价值进行销售的生产者来说,是相当重要的。本文试图解释常常使生产者感到困惑的各种原因。     

安格斯牛×秦川牛F1代产量分级模型的研究

本研究旨在对中长期育肥的安格斯牛×秦川牛F1代的胴体性状指标进行筛选并建立其产量分级模型。随机选取115头发育正常、健康无病的育肥牛屠宰后测定胴体性状指标,采用相关分析和因子分析对其进行筛选并建立产肉率预测模型。结果表明,胴体重、后躯部位肉重、眼肌面积、背膘厚是胴体产量分级的重要指标;将其与产肉率进行多元线性回归,得到胴体产量分级模型,产肉率(%)=41.372―0.046×胴体重+0.403×后躯部位肉重―0.049×眼肌面积+0.334×背膘厚(R2=0.850),经回归诊断和t检验证明该模型预测结果良好,预测值和实测值间差异不显著。结果提示,建立的产量分级模型可用于安格斯牛×秦川牛F1代产量的预测,为开展雪花牛胴体分级提供科学依据。     

安格斯牛×秦川牛F1代产量分级模型的研究

本研究旨在对中长期育肥的安格斯牛×秦川牛F1代的胴体性状指标进行筛选并建立其产量分级模型。随机选取115头发育正常、健康无病的育肥牛屠宰后测定胴体性状指标,采用相关分析和因子分析对其进行筛选并建立产肉率预测模型。结果表明,胴体重、后躯部位肉重、眼肌面积、背膘厚是胴体产量分级的重要指标;将其与产肉率进行多元线性回归,得到胴体产量分级模型,产肉率(%)=41.372-0.046×胴体重+0.403×后躯部位肉重-0.049×眼肌面积+0.334×背膘厚(R²=0.850),经回归诊断和t检验证明该模型预测结果良好,预测值和实测值间差异不显著。结果提示,建立的产量分级模型可用于安格斯牛×秦川牛F1代产量的预测,为开展雪花牛胴体分级提供科学依据。     

美国农业部猪胴体等级划分方法评述

本文评述美国农业部猪胴体等级划分的方法。猪肉生产者追求的是高屠宰率。但是屠宰率通常并不是衡量胴体价值的良好指标,这部分原因是脂肪型猪的屠宰率高于瘦肉型猪。猪肉品质与美国农业部等级美国农业部于80年代中期对阉公猪和小母猪胴体的分级标准进行了修订。阉公猪和小母猪胴体按肉脂品质以及腿臀.腰段、前腿肉和颈肉四个瘦肉分割肉块的期望总量进行分级。该标准采用两级品质,即     

肉牛胴体及肉质评定刍议

本文综述了欧洲经济共同体(EEC)、英国、澳大利亚、比利时等国家和组织所制定的肉牛胴体分类及分级标准,着重介绍了欧洲畜牧学会(EAAP)1983年的胴体分级指标和美国农业部(USDA)所使用的牛肉、犊牛肉和小牛肉的评级办法及标准。同时论述了“肉质”的含义及概念,分别介绍了日本及苏联等国家关于肉质的评定项目及方法;比较分析了影响胴体等级及肉质的遗传及环境因素。最后还对肉牛胴体分级和肉质评定在生产、经营和育种方面的应用作了论述。     

肉用仔鸡加工产量的新标准

当今的肉鸡在许多方面不同于十年前的肉鸡。由于原代育种者进行的遗传选择以及日粮和其他管理技术的改进,今日肉鸡的生长率和饲料效率与以往的肉鸡相比已大大改善。鸡体成分在这十年内也有了变化,因而加工厂使用的胴体、杂碎、分割块和副产品产量的标准值必须更新。本文报道的胴体、杂碎、分割块产量的数值获     

On-line prediction of yield grade,longissimus muscle area,preliminary yield grade,adjusted preliminary yield grade,and marbling score using the MARC beef carcass image analysis system

The present experiment was conducted to evaluate the ability of the U.S. Meat Animal Research Center's beef carcass image analysis system to predict calculated yield grade, longissimus muscle area, preliminary yield grade, adjusted preliminary yield grade, and marbling score under commercial beef processing conditions. In two commercial beef-processing facilities, image analysis was conducted on 800 carcasses on the beef-grading chain immediately after the conventional USDA beef quality and yield grades were applied. Carcasses were blocked by plant and observed calculated yield grade. The carcasses were then separated, with 400 carcasses assigned to a calibration data set that was used to develop regression equations, and the remaining 400 carcasses assigned to a prediction data set used to validate the regression equations. Prediction equations, which included image analysis variables and hot carcass weight, accounted for 90, 88, 90, 88, and 76% of the variation in calculated yield grade, longissimus muscle area, preliminary yield grade, adjusted preliminary yield grade, and marbling score, respectively, in the prediction data set. In comparison, the official USDA yield grade as applied by online graders accounted for 73% of the variation in calculated yield grade. The technology described herein could be used by the beef industry to more accurately determine beef yield grades; however, this system does not provide an accurate enough prediction of marbling score to be used without USDA grader interaction for USDA quality grading.     

The effect of a leptin single nucleotide polymorphism on quality grade, yield grade, and carcass weight of beef cattle

Feedlot producers could optimize the value of cattle in a given market grid if they were able to improve the uniformity of the body composition between cattle among loads. Allelic variation due to a single nucleotide transition (cytosine [C] to thymine [T] transition that results in a Arg25Cys) has been demonstrated to be associated with higher leptin mRNA levels in adipose tissue and increased fat deposition in mature beef, but the effect on economically important carcass traits has not been investigated in either market-ready steers or heifers. Therefore, the objective of this study was to determine the effects of a leptin SNP on the quality grade (QG), yield grade (YG), and weight of beef carcasses. A slaughter trial was conducted using 1,435 crossbred finished heifers and 142 crossbred finished steers as they entered the slaughter facility. Canada QG tended (main effect of genotype P = 0.16, but P < 0.01 for both CC vs. TT and CT vs. TT) to be affected by leptin genotype. Specifically, 7.6 and 7.1% more TT carcasses graded Canada AAA or higher than the CT and CC carcasses, respectively, which supports the suggestion that the leptin SNP is associated with carcass fat. The proportion of carcasses grading Canada YG 1, 2, or 3 was affected (P < 0.01, P = 0.05, and P = 0.02 for YG 1, 2, and 3) by leptin genotype. The proportion of TT carcasses of Canada YG 1 was 12.5 and 15.1% lower than that of CT and CC carcasses, respectively, indicating that rearing animals under the same management and feeding system may result in greater carcass fat and a lower probability of the proportion of carcasses grading YG 1 within certain genotypes. The carcass weights of animals with the CC genotype tended (P = 0.07) to be higher than those of the TT genotype (365.5 vs. 362.3 kg). No significant difference was observed between the TT and CT genotypes in carcass weight. The observed associations between leptin genotype and carcass characteristics may represent an opportunity to genetically identify animals that are most likely to reach specific marketing groups.     

The influence of sex-class, USDA yield grade and USDA quality grade on seam fat trim from the primals of beef carcasses

Beef carcasses (129 steers and 80 heifers) differing in weight, muscling, fatness and marbling score were selected to represent the full spectrum of USDA yield grades; one side was fabricated into boneless primal cuts. Primals were trimmed of all external fat and intermuscular (seam) fat and all components were weighed. Regression equations were developed to predict the percentage of seam fat on an external fat-free primal basis using USDA yield grade (YG), marbling score and a squared function of YG as the independent variables. YG (.77) and marbling score (.67) were highly correlated to seam fat. Heifers tended to have a higher predicted percentage of seam fat than did steers across all YG. Primals from USDA Choice carcasses had approximately 1.0 percentage point more predicted seam fat than did USDA Select primals at the same YG and sex-class. The YG 2.5 heifers had similar proportions of predicted seam fat from primals as YG 3.5 steers, but YG 3.5 heifers tended to have more seam fat than YG 4.5 steers. The same trend was noted between YG 4.5 heifers and YG 5.5 steers, indicating a sex-related deposition of seam fat in fed cattle.     

Accuracy of application of USDA beef quality and yield grades using the traditional system and the proposed seven-grade yield grade system

Beef carcasses (n = 5,542) were evaluated by three USDA on-line graders and compared with the computed expert USDA quality (QG) and yield grades (YG) during 8-h shifts at a major beef-processing facility for a 2-wk period to evaluate the accuracy of applying USDA QG and YG within the traditional five-grade and the proposed seven-grade (segregating YG 2 and 3 into YG 2A, 2B, 3A, and 3B) YG systems. Quality grade distribution of the carcasses was 1.1% Prime, 50.0% Choice, 43.8% Select, and 5.1% No-Roll. Accuracy of applying QG was not affected (P>.05) by changing from the five-grade (91.5%) to either the seven-grade system, when determining only QG (94.3%), or the seven-grade system, when determining QG and YG (95.0%). Calculated expert YG successfully segregated carcasses into their respective YG, but on-line graders could not differentiate between YG 4 and 5 in the seven-grade systems. The application of YG in the five-grade system was more accurate (P<.05) than either of the seven-grade systems. A trend existed for on-line graders to undergrade carcasses as the numerical YG increased. Total accuracy of applying YG decreased by 19.4 to 21.8% when switching from the five-grade to the seven-grade system. The segmentation of USDA YG 2 and 3 into YG 2A, 2B, 3A, and 3B resulted in a decrease in the ability of on-line graders to accurately apply the YG.     

Real-time augmentation of USDA yield grade application to beef carcasses using video image analysis

In two phases, this study assessed the ability of two video image analysis (VIA) instruments, VIASCAN and Computer Vision System (CVS), to augment assignment of yield grades (YG) to beef carcasses to 0.1 of a YG at commercial packing plant speeds and to test cutout prediction accuracy of a YG augmentation system that used a prototype augmentation touchpanel grading display (designed to operate commercially in real-time). In Phase I, beef carcasses (n = 505) were circulated twice at commercial chain speeds (340 carcasses per hour) by 12 on-line USDA graders. During the first pass, on-line graders assigned a whole-number YG and a quality grade (QG) to carcasses as they would normally. During the second pass, on-line graders assigned only adjusted preliminary yield grades (APYG) and QG to carcasses, whereas the two VIA instruments measured the longissimus muscle area (LMA) of each carcass. Kidney, pelvic, and heart fat (KPH) was removed and weighed to allow computation of actual KPH percentage. Those traits were compared to the expert YG and expert YG factors. On-line USDA graders' APYG were closely related (r = 0.83) to expert APYG. Instrument-measured LMA were closely related (r = 0.88 and 0.94; mean absolute error = 0.3 and 0.2 YG units, for VIASCAN and CVS, respectively) to expert LMA. When YG were augmented using instrument-measured LMA and computed either including or neglecting actual KPH percentage, YG were closely related (r = 0.93 and 0.92, mean absolute error = 0.32 and 0.40 YG units, respectively, using VIASCAN-measured LMA; r = 0.95 and 0.94, mean absolute error = 0.24 and 0.34 YG units, respectively, using CVS-measured LMA) to expert YG. In Phase II, augmented YG were assigned (0.1 of a YG) to beef carcasses (n = 290) at commercial chain speeds using VIASCAN and CVS to determine LMA, whereas APYG and QG were determined by online graders via a touch-panel display. On-line grader YG (whole-number), expert grader YG (to the nearest 0.1 of a YG), and VIASCAN- and CVS-augmented YG (to the nearest 0.1 of a YG) accounted for 55, 71, 60, and 63% of the variation in fabricated yields of closely trimmed subprimals, respectively, suggesting that VIA systems can operate at current plant speeds and effectively augment official USDA application of YG to beef carcasses.     

Development of a multivariate yield grade equation to predict compositional traits in mature cow carcasses.

Data from 68 cow carcasses were used to develop a new yield grading system. First principal component (FPC) values for compositional attributes (LNFT = separable lean weight/[lean+fat weight] x 100, LNBN = separable lean weight/[lean+bone+connective tissue weights] x 100, and BTPR = [defatted lean from the round, loin, rib, and chuck]/side weight x 100) were determined. The first component explained 83.5% of the standardized variance and load values were .63, -.52, and .58, respectively. The resulting FPC values ranged from -1.93 to 1.89. The linear regression of LNFT, LNBN, and BTPR (dependent variables) on FPC (independent variable) explained a significant amount of variation (P less than .001) in each case and resulted in R2-values of .98, .67, and .85, respectively. A best-fit yield grade equation, developed to predict FPC, included adjusted fat thickness (ADF), percentage of kidney, pelvic, and heart fat (KPH), and overall muscling grade (OM). The equation, FPC = 2.04 - (.67 x ADF) - (.21 x KPH) - (.0016 x OM), explained a significant amount (P less than .001) of variation in FPC with R2 = .94 and residual standard deviation = .25. Simple correlations for ADF, KPH, and OM with FPC were -.87, -.71, and -.80, respectively. Cow carcasses were assigned to one of three grades based on FPC values that corresponded with predetermined levels of LNFT, LNBN, and BTPR. These grades generally had smaller CV than existing grades. When used in conjunction with quality grades, proposed grades could be more useful to the cow meat industry.     

饲料级油脂的使用

在饲料中添加油脂，对增加饲料的营养功能，改善饲料的性能、提高饲料的效率有很好的效果。油脂在饲料中的作用和效能，已受到油脂和畜禽饲养业界的普遍重视。     

Technical note: the United States Department of Agriculture beef yield grade equation requires modification to reflect the current longissimus muscle area to hot carcass weight relationship

With the adoption of visual instrument grading, the calculated yield grade can be used for payment to cattle producers selling on grid pricing systems. The USDA beef carcass grading standards include a relationship between required LM area (LMA) and HCW that is an important component of the final yield grade. As noted on a USDA yield grade LMA grid, a 272-kg (600-lb) carcass requires a 71-cm(2) (11.0-in.(2)) LMA and a 454-kg (1,000-lb) carcass requires a 102-cm(2) (15.8-in.(2)) LMA. This is a linear relationship, where required LMA = 0.171(HCW) + 24.526. If a beef carcass has a larger LMA than required, the calculated yield grade is lowered, whereas a smaller LMA than required increases the calculated yield grade. The objective of this investigation was to evaluate the LMA to HCW relationship against data on 434,381 beef carcasses in the West Texas A&M University (WTAMU) Beef Carcass Research Center database. In contrast to the USDA relationship, our data indicate a quadratic relationship [WTAMU LMA = 33.585 + 0.17729(HCW) -0.0000863(HCW(2))] between LMA and HCW whereby, on average, a 272-kg carcass has a 75-cm(2) (11.6-in.(2)) LMA and a 454-kg carcass has a 96-cm(2) (14.9-in.(2)) LMA, indicating a different slope and different intercept than those in the USDA grading standards. These data indicate that the USDA calculated yield grade equation favors carcasses lighter than 363 kg (800 lb) for having above average muscling and penalizes carcasses heavier than 363 kg (800 lb) for having below average muscling. If carcass weights continue to increase, we are likely to observe greater proportions of yield grade 4 and 5 carcasses because of the measurement bias that currently exists in the USDA yield grade equation.     

SV繁育规则和分级规则和分级

SV是德国牧羊犬协会的德文缩写.它成立于1898年,现总部设在德国奥克斯堡,下设繁育部、训练部、秘书处及商务部,全国各州有分部,各地有数百个分支机构.总部设主席1人,副主席若干人,秘书长1人,现有专职工作人员70余人,各地有分部的负责人.主要任务是进行犬的血统管理、制定各种规则标准、进行繁育管理、开展犬展比赛、组织优良犬出口等,现将该协会的繁育规则和种犬分级编译如下(©Martin Wahl 1997).     

不同小黑麦品种的种子产量及产量构成因素比较

以32份小黑麦品种为试验材料,研究了各品种的产量构成因素（穗长、小穗数,穗粒数、穗粒质量）以及他们之间的相关性。结果表明,小黑麦品种OH2276株高最高,为136．77cm,但其种子产量低;安新麦83—25的综合性状最优,穗粒数最多,为J09。19;北联4号,89D-8和DH796综合性状也较好,具有良好的应用前景;HB200上的综合性状最差,HB200下和OB74的综合性状较差。