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Data from 655 treatments of 116 studies were used in a meta-analysis to determine the daily digestible energy (DE), metabolizable energy (ME) and net energy (NE) intake of Chinese growing-finishing pigs, and to predict feed efficiency responses to change in dietary DE, ME and NE. Three alternative functions (i.e., polynomial, Bridges and asymptotic function) were employed for fitting daily DE, ME or NE intakes to mean body weight. The results showed that the three models from the current study provided reasonable fit (all R2 > 0.83) for the energy intake data. However, under the same energy system, the polynomial function had the smallest Akaike's information criteria (AIC) and residual standard deviation (RSD), followed by Bridges and asymptotic functions. The three model-generated energy intakes of growing pigs were significantly less than that of the Chinese Feeding Standard of Swine, but similar to that of the National Research Council (2012), while the values of finishing pigs were greater than both standards. Compared with those that predict feed efficiency based on DE or ME, the equation with NE as a predictor had the minimized AIC and RSD. It was also found that feed efficiency increased with increasing dietary energy density (DED), but this response varied with pig body weight, and the lighter pigs were more sensitive to DED than heavier pigs.

The growth of plants and animals can be described through a growth curve. This curve is given by the equation of a nonlinear model, such as the Logistic model and the Gompertz model. The objective of this study was to adjust the Chanter model, as well as Logistic and Gompertz, using a set of cocoa (clone Sial-105) fruit whose length and diameter measurements were evaluated from 30 to 180 days after pollination, every 15 days. The Chanter model is a hybrid between the Logistic model and Gompertz model whose parameters can be interpreted similarly. A comparison of the quality of fit between the models was made using the following statistical measures: the Akaike’s information criterion (AIC), the Akaike’s weights criterion, Bayesian information criterion (BIC), residual standard deviation (RSD),the adjusted coefficient of determination (R²aj) and the measures of non-linearity Box’s bias and curvature of Bates and Watts. It was verified that the Chanter model is the most suitable one among the studied models for modeling the cocoa data. RESUMO: O crescimento de plantas e animais pode ser descrito por meio de uma curva. Essa curva é dada pela equação de um modelo não linear, como o modelo Logístico e o modelo Gompertz. O objetivo deste trabalho foi ajustar o modelo Chanter, assim como o Logístico e Gompertz, utilizando um conjunto de dados do fruto do cacaueiro do clone SIAL - 105, cujas medidas de comprimento e diâmetro foram avaliadas de 30 até 180 dias após a polinização, a cada 15 dias. O modelo Chanter é um híbrido entre o modelo Logístico e o modelo Gompertz cujos parâmetros podem ser interpretados similarmente. A avaliação da qualidade do ajuste entre os modelos foi feita utilizando as seguintes medidas estatísticas: o critério de informação de Akaike (AIC), o critério Peso de Akaike, o critério de informação de Bayes (BIC), o desvio padrão residual (DPR), o coeficiente de determinação ajustado (R²aj) e as medidas de não linearidade, vício de Box e curvatura de Bates e Watts. Verificou-se que o modelo Chanter dentre os modelos estudados neste trabalho é o mais adequado para o ajuste aos dados do fruto do cacaueiro.

The prediction of carcass composition in Hanwoo steers is very important for value-based marketing, and the improvement of prediction accuracy and precision can be achieved through the analyses of independent variables using a prediction equation with a sufficient dataset. The present study was conducted to develop a prediction equation for Hanwoo carcass composition for which data was collected from 7,907 Hanwoo steers raised at a private farm in Gangwon Province, South Korea, and slaughtered in the period between January 2009 and September 2014. Carcass traits such as carcass weight (CWT), back fat thickness (BFT), eye-muscle area (EMA), and marbling score (MAR) were used as independent variables for the development of a prediction equation for carcass composition, such as retail cut weight and percentage (RC, and %RC, respectively), trimmed fat weight and percentage (FAT, and %FAT, respectively), and separated bone weight and percentage (BONE, and %BONE), and its feasibility for practical use was evaluated using the estimated retail yield percentage (ELP) currently used in Korea. The equations were functions of all the variables, and the significance was estimated via stepwise regression analyses. Further, the model equations were verified by means of the residual standard deviation and the coefficient of determination (R(2)) between the predicted and observed values. As the results of stepwise analyses, CWT was the most important single variable in the equation for RC and FAT, and BFT was the most important variable for the equation of %RC and %FAT. The precision and accuracy of three variable equation consisting CWT, BFT, and EMA were very similar to those of four variable equation that included all for independent variables (CWT, BFT, EMA, and MAR) in RC and FAT, while the three variable equations provided a more accurate prediction for %RC. Consequently, the three-variable equation might be more appropriate for practical use than the four-variable equation based on its easy and cost-effective measurement. However, a relatively high average difference for the ELP in absolute value implies a revision of the official equation may be required, although the current official equation for predicting RC with three variables is still valid.

Two experiments were conducted to determine the digestible energy (DE) and metabolizable energy (ME) contents of corn gluten feed (CGF) for finishing pigs and to develop equations predicting the DE and ME content from the chemical composition of the CGF samples, as well as validate the accuracy of the prediction equations. In Exp. 1, ten CGF samples from seven provinces of China were collected and fed to 66 finishing barrows (Duroc×Landrace×Yorkshire) with an initial body weight (BW) of 51.9±5.5 kg. The pigs were assigned to 11 diets comprising one basal diet and 10 CGF test diets with six pigs fed each diet. The basal diet contained corn (76%), dehulled soybean meal (21%) and premix (3%). The ten test diets were formulated by substituting 25% of the corn and dehulled soybean meal with CGF and contained corn (57%), dehulled soybean meal (15.75%), CGF (24.25%) and premix (3%). In Exp. 2, two additional CGF sources were collected as validation samples to test the accuracy of the prediction equations. In this experiment, 18 barrows (Duroc×Landrace×Yorkshire) with an initial BW of 61.1±4.0 kg were randomly allotted to be fed either the basal diet or two CGF containing diets which had a similar composition as used in Exp. 1. The DE and ME of CGF ranged from 10.37 to 12.85 MJ/kg of dry matter (DM) and 9.53 to 12.49 MJ/kg of DM, respectively. Through stepwise regression analysis, several prediction equations of DE and ME were generated. The best fit equations were: DE, MJ/kg of DM = 18.30-0.13 neutral detergent fiber-0.22 ether extract, with R(2) = 0.95, residual standard deviation (RSD) = 0.21 and p

The objective of this study was to identify an appropriate mathematical function describing the in vitro phytate degradation profile of soaked wheat or barley (20 °C; 1 cereal: 2.75 water (w/w)) as affected by heat-treatment (stem pelleting at 90 °C) and phytase addition (Phytase 1: 0, 250, 500, 750, 1000, 1500, 2000 or 2500 FTU/kg; Phytase 2: 0, 375, 750, 1125, 1500, 2250, 3000 or 3750 FYT/kg). Samples were collected at 0, 2, 4, 8 and 24 h of soaking. The full dataset was split in two sub-sets as Phytase 1 and Phytase 2 are defined by different phytase activities. A segmented linear (SL) function, a first-order (FO) function and a Generalised Michaelis Menten (GMM) function were considered. The GMM fitted the data best. The GMM function was used to derive the relative instantaneously degradable fraction of phytate ( F 0 ) and the half-life ( t 1/2) of phytate in hours ( K ). Addition of Phytase 1 or Phytase 2 had no effect on the degradation of phytate. The F 0 was greater in the heat-treated barley compared with the heat-treated wheat (0.19 vs. 0.14; P=0.02; Phytase 1). Heat-treatment of the cereals increased the F 0 from 0.05 to 0.15 (P=0.0007; Phytase 2). The K was lower in the non-heat-treated wheat compared with the non-heat-treated barley (averaging 23.3 vs. 43.5 h), whereas K was higher in the wheat compared with the barley (averaging 12.3 vs. 11.1 h; P=0.02 and P=0.006; Phytase 1 and Phytase 2) when the cereals were heat-treated. In conclusion, the GMM function is a suitable model for describing the phytate degradation profiles of the soaked wheat and barley. The in vitro results showed that heat-treatment of the cereals increased the F 0 and reduced the K although the plant phytase activity was reduced. This was possible due to induced structural changes of the grain that increased the contact between phytate and plant phytases. The lack of effect of additions of Phytase 1 or Phytase 2 on the phytate degradation in the cereals was possible due to a poor access of the microbial phytases to phytate. Therefore, more knowledge is needed about processing technologies that increase the access of plant and microbial phytases to phytate in the grains.

To identify fleece and animal growth characteristics that are associated with cashmere production, the magnitude and direction of factors affecting cashmere production using fleece and live body weight measurements available to the cashmere producer were modelled. Data was collected from 11 Australian commercial cashmere farms. Following log transformation of clean cashmere weight, the best general linear model for predicting clean cashmere weight included: farm, mean fibre diameter, fibre diameter standard deviation, cashmere fibre curvature and fibre curvature standard deviation, cashmere staple length, clean washing yield, live body weight, live body weight change, and interactions between these parameters. The best general linear model did not include variables for age and sex cohorts. The variance accounted for was 67.6%. The residual standard deviation was 0.115. A majority of the variation regarding clean cashmere production (58%) was related to farm, staple length and mean fibre diameter. To obtain an understanding of how the responses of clean cashmere weight to fleece and growth measurements varied with farm, regression coefficients in the general linear model were modelled as possibly correlated random effects, using REML mixed model analysis. On a typical farm, greater clean cashmere weight was associated with longer staple length, greater mean fibre diameter, greater fibre diameter standard deviation, greater fibre curvature, greater fibre curvature standard deviation and greater live body weight. Farms recorded responses varying from negative to positive for washing yield and live body weight change. It was concluded that fleece characteristics and animal growth (but not age and sex) are primary determinants of cashmere production. However, the way these characteristics affect cashmere production varies from farm to farm.

Changes in physical body size during gestation were monitored using 529 sets of sow measurements. All sows were from the same herd and production system with a range in parity from 1 to 8. Sows were individually weighed, P2 backfat thickness was determined by ultrasound and morphometric measurements of body size were taken five times during gestation: day 0 (at service), day 25, day 50, day 80 and day 110. The morphometric measurements included sow height (from floor to last rib at the midline, from floor to ventral surface and from floor to hip), heart girth, depth of last rib, length (from snout to tail and from anterior scapula to tail) and width (at ham, at last rib and at shoulder). Regression analyses were used to model the relationship between day of gestation or parity number and morphometric measurements of body size. Regression equations were also developed to estimate sow weight from physical measurements, day of gestation and parity. As expected, sow dimensions, in general, increased as pregnancy progressed and also with increasing parity number. The relationships between day of gestation and body dimensions were described by linear and quadratic regression models, which had a range of adjusted R2 values up to 0.99. Similar relationships to parity number had a range of R2 values between 0.51 and 0.96. Sow depth, which can be used as an estimate of the width of the sow when lying, equalled the maximum width of the gestation stall (650 mm) at day 103 of gestation. However, by day 40 of gestation, predicted mean sow depth (570 mm) equalled the width at the rear of the crate. The implication of this is that after day 40 of gestation, the average sow was too wide for the rear of the crate when lying in a recumbent position. On day 110 of gestation, 95% of the mean sow body depths would be accommodated in stalls that were 674 mm wide; however, the range in body sizes with increasing parity number suggests the use of more than one stall width would be appropriate. Sow weight could be estimated with an adjusted R2 value of 0.81 and with a residual standard deviation (r.s.d.) of 16.5 kg using heart girth alone, or more accurately using a model with parity, day of gestation, P2 backfat depth and heart girth as the parameters (R2 = 0.89, r.s.d. 12.4 kg).

The potential of a gas production technique (GPT) to predict fractions of rumen fermentable organic matter (OMf), crude protein (CP) and starch escaping rumen degradation (EP and ESTA), as well as in vitro organic matter digestibility (OMd) were studied with 30 samples of maize silage. Rumen degradation parameters were determined with four cannulated lactating cows by incubating finely cut fresh silage in nylon bags for 0, 12, 24, 48, 72 and 288 h. In vitro OMd was measured according to Tilley and Terry [Tilley, J.M.A., Terry, R.A., 1963. A two-stage technique for the in vitro digestion of forage crops. J. Br. Grassl. Soc. 18, 104–111], using rumen fluid from two sheep. Gas production (GP) was continuously measured by incubating dried and ground samples in buffered rumen fluid from two sheep for 72 h, and GP curves were analysed with a three-phase model. Parameters studied were GP in the first 3 h of fermentation (GP 3 h ), between 3 and 20 h (GP 3–20 h ), the time to reach half of these values ( b 1 and b 2 ), the maximum fermentation rate during the second phase ( R 2 max ) and total GP, and OM fermented after 72 h of incubation (GP 72 h and OMf 72 h ). Multiple regression was used to predict OMf, EP, ESTA and OMd based on GP parameters and chemical composition (i.e., DM, NDF, starch, CP). Fractions of OMf, EP, ESTA and OMd were 0.580 ± 0.040, 0.350 ± 0.062, 0.260 ± 0.106 and 0.732 ± 0.028, respectively. The GP 3 h , GP 3–20 h and GP 72 h were 52 ± 5, 177 ± 13 and 323 ± 15 ml/g OM, respectively, b 1 and b 2 were 0.9 ± 0.2 and 8.7 ± 0.6 h, respectively, and R 2 max was 15.6 ± 1.6 ml/h. All rumen degradability parameters could be predicted by a two-term regression with b 2 as most important parameter. For OMf, the combination of b 2 and GP 3 h resulted in a determination coefficient ( R 2 ) of 50.4% and a residual standard deviation (R.S.D.) of 0.028. The EP could be predicted by b 2 and CP ( R 2 = 68.9%; R.S.D. = 0.035) and ESTA by b 2 and DM ( R 2 = 74.3%; R.S.D. = 0.054). In the case of OMd, OMf 72 h was the single explaining variable ( R 2 = 77.9%; R.S.D. = 0.013).

This study determined if the in vitro apparent dry matter digestibility (DMD) of feed samples in ANKOM DaisyII incubators is affected when alternatives to ANKOM F57 bags are used, or when samples are weighted to ensure submersion in the media. In three experiments, feed samples were incubated within filter bags for 48 h in buffered rumen fluid and for a further 48 h in acidified pepsin in ANKOM DaisyII incubators. In Experiment 1, 32 replicates of three dried, ground (1 mm) feed samples were either weighed into ANKOM F57 polyester/polyethylene bags with a pore size of 25 μm or weighed into dacron bags with a pore size of 50 μm. One 5 g glass ball was placed in each of eight ANKOM bags or eight dacron bags for each forage to ensure submersion in the media. In Experiments 2a and 2b, 16 and 8 replicates of 14 dried, ground feed samples were respectively, weighed into either ANKOM F57 bags or polyester/polyethelene bags with a pore size of 30 μm. The DMD of these samples was also determined simultaneously using the conventional, tube based, rumen fluid pepsin technique. Regression relationships were derived between ANKOM and alternative bag DMD estimates. In Experiment 1, weighted bags had greater (P

The objectives were to develop equations for predicting fat-free lean in swine carcasses and to estimate the prediction bias that was due to genetic group, sex, and dietary lysine level. Barrows and gilts (n = 1,024) from four projects conducted by the National Pork Board were evaluated by six procedures, and their carcass fat-free lean was determined. Pigs of 16 genetic groups were fed within weight groups one of four dietary regimens that differed by 0.45% in lysine content and slaughtered at weights between 89 and 163 kg. Variables in equations included carcass weight and measures of backfat depth and LM. Fat-free lean was predicted from measures of fat and muscle depth measured with the Fat-O-Meater (FOM), Automated Ultrasonic System (AUS), and Ultrafom (UFOM) instruments, carcass 10th-rib backfat and LM area (C10R), carcass last-rib backfat (CLR), and live animal scan of backfat depth and LM area with an Aloka 500 instrument (SCAN). Equations for C10R (residual standard deviation, RSD = 2.93 kg) and SCAN (RSD = 3.06 kg) were the most precise. The RSD for AUS, FOM, and UFOM equations were 3.46, 3.57, and 3.62 kg, respectively. The least precise equation was CLR, for which the RSD was 4.04 kg. All procedures produced biased predictions for some genetic groups (P < 0.01). Fat-free lean tended to be overestimated in fatter groups and underestimated in leaner ones. The CLR, FOM, and AUS procedures overestimated fat-free lean in barrows and underestimated it in gilts (P < 0.01), but other procedures were not biased by sex. Bias due to dietary lysine level was assessed for the C10R, CLR, FOM, and SCAN procedures, and fat-free lean in pigs fed the low-lysine dietary regimen was overestimated by CLR, FOM, and SCAN (P < 0.05). Positive regressions of residuals (measured fat-free lean minus predicted fat-free lean) on measured fat-free lean were found for each procedure, ranging from 0.204 ± 0.013 kg/kg for C10R to 0.605 ± 0.049 kg/kg for UFOM, indicating that all procedures overestimated fat-free lean in fat pigs and underestimated it in lean pigs. The pigs evaluated represent the range of variation in pigs delivered to packing plants, and thus the prediction equations should have broad application within the industry. Buying systems that base fat-free lean predictions on measures of carcass fat depth and muscle depth or area will overvalue fat pigs and undervalue lean pigs.

This study assessed the efficacy of the n-alkane technique to estimate intake and diet composition in animals given single foods or a choice of two. In the first experiment intakes of pelleted ryegrass and lucerne, given either alone or as a choice, were measured in lambs housed indoors in individual pens. Each of the three feeding treatments was given to 12 lambs at two degrees of maturity (0·30 and 0·45 of estimated mature sizes). The 12 lambs were constituted as three replicates of the two sexes of each of two breeds. The measured intakes were compared with those estimated using the n-alkanes C31and C33, found naturally in the foods, and C32, which was given as a dose. On the choice treatment diet composition was estimated using a non-negative least squares procedure and data on C31and C33alone. The agreement between actual and predicted intake was good:R2of 0·938 for log-linear regression with a residual standard deviation of 0·0845. Intake of lucerne when offered alone was slightly yet significantly over-predicted. The proportion of ryegrass in the diet was also accurately predicted (R2of 0·950 and residual s.d. of 0·0398). Using the data on C27and C29, in addition to that on C31and C33, gave a poorer agreement with the observed diet compositions. The low and similar levels of C27in the two foods meant that this n-alkane provided little extra information that could be used to estimate diet composition. In a second experiment faecal samples were collected every 4 hours over a 24-h period in six lambs on ad libitum, and in six lambs on a restricted quantity, of pelleted ryegrass. There was no significant diurnal variation in the ratios of either C31or C33to C32on either ad libitum or restricted feeding. The time of faecal collection within a day should not therefore affect the reliability of the predictions. The study confirmed the value of using n-alkanes in methods to determine the intake of forages by sheep, and that the time of faecal collection within the day does not affect the reliability of these predictions. The results also confirm the utility of the n-alkane method for estimating diet choice, at least with two-component mixtures.

The use of feed intake behaviour traits, obtained from electronic feeding stations, and feed intake curve parameters to genetically improve performance traits was analysed. Daily feed intake and feed intake behaviour of 5601 group-penned boars of two dam lines were recorded by electronic feeders in weeks 1, 3, 5, 7 and 9 during 10 weeks (100–170 d) on performance test. Additionally, performance test traits and parameters of an individually fitted linear-quadratic regression of feed intake on time on test, were available. A multiple trait animal model considering observed feed intake and feed intake behaviour at each test week as a different trait was used. Estimated heritabilities for feed intake and feed intake behaviour of the entire test period were 0.39, 0.46, 0.34, 0.44, 0.44 and 0.41 for daily feed intake, time per day, visits per day, time per visit, feed intake per visit, and feed intake rate, respectively. Heritabilities for behavioural traits in each test week were below estimates of entire test and showed lower variation, except for daily feed intake and time per day. Residual standard deviation of feed intake using linear-quadratic regression on time on test showed a moderate heritability of 0.22. Genetic correlations of feed intake behaviour traits indicate that number of visits per day was independent from growth performance, while time per day was genetically associated with average daily gain and daily feed intake (0.31 and 0.41). In contrast, visits per day and related traits such as time per visit and feed intake per visit were genetically correlated with residual standard deviation of linear-quadratic regression (−0.28, 0.31 and 0.39). Feed intake per visit in the seventh test week resulted in the highest genetic correlation (0.45) with residual standard deviation of the feed intake curve. This residual standard deviation and related traits will be of increasing interest for future breeding programs in order to obtain high performances in a wide range of environments.

Carcass and live measurements of 45 barrows were used to evaluate the magnitude of ractopamine (RAC) treatment prediction biases for measures of carcass composition. Barrows (body weight = 69.6 kg) were allotted by weight to three dietary treatments and fed to an average body weight of 114 kg. Treatments were: 1) 16% crude protein, 0.82% lysine control diet (CON); 2) control diet + 20 ppm RAC (RAC16); 3) a phase feeding sequence with 20 ppm RAC (RAC-P) consisting of 18% crude protein (1.08%. lysine) during wk 1 and 4, 20% crude protein (1.22% lysine) during wk 2 and 3, 16% crude protein (0.94% lysine) during wk 6, and 16% crude protein (0.82% lysine) during wk 6. The four lean cuts from the right side of the carcasses (n = 15/treatment) were dissected into lean and fat tissue. The other cut soft tissue was collected from the jowl, ribs, and belly. Proximate analyses were completed on these three tissue pools and a sample of fat tissue from the other cut soft tissue. Prediction equations were developed for each of five measures of carcass composition: fat-free lean, lipid-free soft tissue, dissected lean in the four lean cuts, total carcass fat tissue, and soft-tissue lipid mass. Ractopamine treatment biases were found for equations in which midline backfat, ribbed carcass, and live ultrasonic measures were used as single technology sets of measurements. Prediction equations from live or carcass measurements underpredicted the lean mass of the RAC-P pigs and underpredicted the lean mass of the CON pigs. Only 20 to 50% of the true difference in fat-free lean mass or lipid-free soft-tissue mass between the control pigs and pigs fed RAC was predicted from equations including standard carcass measurements. The soft-tissue lipid and total carcass fat mass of RAC-P pigs was overpredicted from the carcass and live ultrasound measurements. Prediction equations including standard carcass measurements with dissected ham lean alone or with dissected loin lean reduced the residual standard deviation and magnitude of biases for the three measures of carcass lean mass. Prediction equations including the percentage of lipid of the other cut soft tissue improved residual standard deviation and reduced the magnitude of biases for total carcass fat mass and soft-tissue lipid. Prediction equations for easily obtained carcass or live ultrasound measures will only partially predict the true effect of RAC to increase carcass leanness. Accurate prediction of the carcass composition of RAC-fed pigs requires some partial dissection, chemical analysis, or alternative technologies.

Using the Fossomatic method (FSCC) a total of 23,003 analytical SCC observations were carried out on 6400 aliquots taken from 80 individual ewe milk samples with the objective of studying the influence of 4 preservation procedures (without preservation, potassium dichromate, azidiol, and bronopol), 2 storage temperatures (ambient and refrigeration), 10 milk ages (3,6,12, and 24h, and 2,3,4,5,7, and 9d postcollection), and two analytical temperatures (40 and 60 degrees C). In addition, each sample was analyzed with direct microscopic method (DMSCC), using 3 different stainings for each sample: methylene blue (MB), May-Grünwald-Giemsa (MGG) and Pyronin Y-methyl green (PGM). This allowed DMSCC and FSCC (at 24 h of age) to be compared. The reference DMSCC from MB staining was a reliable method in ewe milk, though more specific stainings such as MGG and PMG slightly improve the residual standard deviation for repeated SCC. Between DMSCC and FSCC, the highest coefficients of correlation (0.972 to 0.996) corresponded to preserved and refrigerated milk, and the lowest (0.708 to 0.919) to unpreserved and ambient stored aliquots. Except for the unpreserved and ambient stored aliquots, SCC values were similar in all aliquots. Under FSCC, preservation, storage and analytical temperature, milk age, and most of the interactions showed a significant effect on SCC variation. In preserved samples, logSCC values ranged between 5.67 (bronopol) and 5.62 (azidiol). The higest values (5.72) were for unpreserved milk, which showed false overestimation of SCC due to bacterial proliferation. LogSCC was higher at 60 degrees C (5.68) than at 40 degrees C (5.65). The interaction between age, preservation and storage temperature showed no cell degeneration in properly handled samples over the 9 d of study.

Alternative versions of a common three-parameter nonlinear growth function were evaluated on two groups of gilts. Gilts were randomly assigned to be reared under all-in, all-out (AIAO; n = 96) or continuous flow (CF) management (n = 96). The addition of a single random effect, in which the mature BW of each pig varies, provided a substantially better fit and smaller parameter standard errors. This model predicts a constant coefficient of variation between pigs. The addition of a second random effect further improved the likelihood statistics and reduced the residual standard deviation, although the impact was much smaller. The inclusion of the second random effect accounts for different patterns of growth between pigs. Variations in the growth patterns allow greater fleiibility to describe the underlying variance/covariance structure of the serial live BW. For all miied effects models, the mean and approiimate variation in age required for pigs to reach a specific BW can be predicted. After 104 d of age, the growth of CF gilts declined more rapidly, and the standard deviation in days required to reach specific target BW (TBW) (110, 120, or 130 kg) increased more rapidly than for the AIAO gilts. These models are also easily adaptable to stochastic modeling.

Foram utilizadas 48 fêmeas suínas, para se determinar o rendimento de cortes comerciais, após abate e dissecação das carcaças dos animais aos 50, 60, 70, 80, 90, 100, 110 e 120 kg. Foram tomadas medidas ultra-sônicas in vivo para área de olho de lombo e espessura de toucinho. Os preditores utilizados para estimar o rendimento de carne magra foram espessura de toucinho e área de olho de lombo, além das medidas feitas nos diferentes cortes, como peso do corte do pernil, peso do músculo do pernil, peso do músculo da paleta, peso do músculo do carré, peso do músculo da copa, e assim por diante, além dos dados de peso de músculo da carcaça total, peso da gordura da carcaça total, peso de ossos da carcaça total e peso de pele da carcaça total. Foram desenvolvidas equações de predição por meio de regressão linear simples e múltipla, a partir dos dados da dissecação e das medidas in vivo da área de olho de lombo, feitas por ultra-sonografia em tempo real. As equações de predição para quantificar os principais componentes dos cortes suínos tiveram ótimo ajuste, com R2 superiores a 0,84 para músculos e desvio-padrão residual de 0,62 a 0,94. A técnica de ultra-sonografia em tempo real apresenta alta correlação com as medidas tomadas nas carcaças dos suínos e, por isso, estima a quantidade de carne na carcaça e o rendimento dos cortes comerciais com grande precisão. Concluiu-se que a técnica de ultra-sonografia em tempo real é acurada para predizer as principais características das carcaças dos suínos medidas in vivo. Forty-eight female pigs were used to determine the commercial cut yields after slaughter of the animals at 50, 60, 70, 80, 90, 100, 110 and 120 kg and dissection of the carcasses. Life ultrasonic measures were taken to measure the longissimus muscle area and for backfat thickness. The predictors used to estimate the lean meat yield were backfat thickness and longissimus muscle area. Besides the measures made in the different cuts, as ham cut weight, ham muscle weight, palette muscle weight, and carré muscle weight and pantry muscle weight. Additionally, the data of the total carcass muscle weight, total carcass fat weight, total carcass bone weight and total carcass skin weight, were also used. Prediction equations were developed using simple and multiple linear regressions using the logistic model from the dissection data and from the live ultrasonic measure data. Prediction equations were developed through out simple and multiple linear regressions using a logistic model from the dissection and live measures made by real-time ultrasound technology data. The prediction equation to determine the amounts the main components of the meat pork cuts presented a great adjustment, with R2 superiors to 0.84 for muscles, with residual standard deviation (RSD) 0.62 to 0.94. The real-time ultrasonograph technology presented high correlation with the measures taking in the pork carcasses and could be used to estimate the amount of lean meat in the pork carcasses and also to estimate the commercial pork cut yields with great precision. It was concluded that the real-time ultrasonograph technology is accurate to predict the main pork carcass traits measured in the live animal.

Foram utilizadas 48 fêmeas suínas para se determinarem as curvas de crescimento de tecidos, após abate e dissecação das carcaças dos animais aos 50, 60, 70, 80, 90, 100, 110 e 120 kg. Os cortes realizados nas carcaças foram feitos de acordo com aqueles utilizados pela indústria. Em cada corte, separaram-se as quantidades de músculo, gordura, ossos e pele, para o cálculo de rendimento de tecidos. Foi utilizada a função alométrica estendida, para estimar e ajustar as curvas de crescimento. A quantidade de músculo na carcaça apresentou crescimento variando de 41,9 nos animais de 50 kg, a 44,1%, nos animais de 120 kg. A função alométrica estendida ajustou a curva para quantidade de músculo, com R2 = 0,68 e desvio-padrão residual de 1,42. Os valores de R2 para porcentagem de gordura foram de 0,80 e os de desvio-padrão residual, de 6,35, com aumento de 12,7 a 18,3% entre 50 e 120 kg. Quanto às curvas de crescimento para rendimento de cortes comerciais, os valores de R2 foram variáveis, porém com desvios-padrão residuais baixos. Concluiu-se que a função alométrica estendida é acurada para estimar curvas de crescimento de tecidos calculados, em porcentagens do peso de suínos vivos abatidos entre 50 e 120 kg de peso corporal; a deposição de gordura é proporcionalmente maior que a deposição de tecido muscular; e os suínos depositam, em termos proporcionais, tecido muscular e gordura de forma crescente. Forty-eight female pigs were used to determine tissue growth curves after slaughter of the animals at 50, 60, 70, 80, 90, 100, 110 and 120 kg and dissection of the carcasses. The lean cuts in carcass were made according to that used by Brazilian pork slaughters. In each cut it were separated muscle, fat, bone and skin amounts. The expanded allometric function was used to estimate and to fit the growth curves. The muscle amounts in carcass increased to 41.9% for animals at 50 kg and to 44.1% for animals at 120 kg. The expanded allometric function fitted the curve to muscle amount with R2 = 0.68 and the residual standard deviation RSD = 1.82. The R2 value for fat percentage was 0.80% and the (RSD) value was 6.35 with increase of 12.7% to 18.3% between animals at 50 to 120 kg. Concerning to the growth curves for the commercial meat cut yields, the R2 value was variable, although with a RSD low. It was concluded that the expanded allometric function is accurate to estimate swine time growth curves calculated in percentage of weight of swine, slaughter between 50 and 120 kg and that fat deposition is proportionally higher than muscle deposition and that muscle and fat deposition, in percentage of carcass, increase from 50 to 120 kg.

Mature weight for Large White sows was estimated as the asymptote of a negative exponential growth curve. The data consisted of 2620 weight recordings from 147 sows for the period 1995?1999. Body weight was recorded every 2 weeks from 150 days of age until first service and at parturition and weaning in each parity. A starting point of 150 days was chosen in order to fit the Brody function to the data. Estimates of mature weight in relation to the number of records available (5?10, 11?15, 16?20 and >20) per sow were 285, 258, 260 and 270 kg, respectively. The goodness of fit was calculated as a residual standard deviation (9.7?15.4). No significant effect on mature weight was found for feeding level during rearing or age at first service. The estimated mature weights increased with the number of records available and was more reasonable if the weights were collected throughout the growth and reproductive period.

Vaginal and tympanic membrane temperatures were simultaneously monitored in nine beef heifers. Data not associated with ear infections were characterized by respective vaginal and tympanic membrane temperatures of 38.81 and 38.58 °C. A close relationship between the two temperatures was indicated by a correlation of 0.77 and residual standard deviation of 0.29 °C. Key words: Cattle, radiotelemetry, thermoregulation, tympanic membrane, vagina

The objectives of this experiment were to determine the in vivo digestibility of solvent extracted and expeller palm kernel meal (PKM) and to establish how well this was predicted by laboratory techniques. Eight samples of expeller PKM and four samples of solvent extracted PKM were collected over 4 years. Their in vivo digestibility was measured by total faecal collection using four wethers per sample. The laboratory methods for predicting digestibility were the in vitro rumen fluid (RF), neutral detergent cellulase with gammanase (NCG), and pepsin cellulase with gammanase (PCG) methods. Gammanase has been added to the latter two methods specifically to improve the prediction of digestibility of PKM. The solvent extracted samples had higher organic matter digestibility (691 vs. 653 g/kg, sed 15.7) and crude protein digestibility (727 vs. 597 g/kg, sed 36.6). However, the expeller samples had higher ( P =0.12) contents of digestible energy (13.4 vs. 12.5 MJ/kg DM, sed 0.48) due to their higher gross energies (20.6 vs. 19.1 MJ/kg DM). None of the laboratory digestibility tests predicted digestibility satisfactorily. The residual standard deviation was 29, 32 and 31 g/kg for the RF, NCG and PCG methods, respectively. In particular, two expeller samples and two solvent extracted samples were grossly underpredicted by the laboratory methods. These results indicate that PKM is a medium quality energy feed for ruminants and that enzymatic procedures including gammanase or the in vitro rumen fluid method do not accurately predict its digestibility.

Knowledge of saleable meat yield (SMY%) at the time of carcass classification enables payments to be made on the basis of carcass saleable meat content. This study determined the extent to which improved accuracy of estimating SMY% enabled larger premiums to be paid for better yielding carcasses. A population of 1000 carcasses was simulated for true SMY% values (275 kg carcass weight; mean SMY% of 66 with a standard deviation of 3.0) as well as estimated SMY% values when accuracies in terms of residual standard deviation (RSDs) ranged from 0 to 2.5. A consequence of the requirement that estimates of SMY% be unbiased was that variability of the estimates was less than that of the true SMY% when estimation was imperfect. Premiums that could be paid to the top 5% of carcasses relative to the average increased by 5.8, 9.2, and 9.7 c kg−1 as the RSD decreased from 2.5 to 1.0 for five‐step, eight‐step, and smooth payment systems, respectively. The rate at which potential premiums increased with decreas...

An exponential curve, W = P - Q exp (-Rt) was fitted to egg weights (W) of individual hens from 8 genetic groups tested for egg production from 18-51 weeks of age (t). The groups were constituted of the combination of genotype at the sex-linked dwarfism locus (normal or dwarf) and line (White Leghorn, Brown Egg and both reciprocal crosses). The least-squares mean of the residual standard deviation about the curve was between 1.27 and 1.74 g in the 8 groups and estimated values of the initial P-Q and mature egg P weights were between 26.9 and 33.9 g and 56.9 and 65.8 g, respectively. Effects of genotype and line as well as heterosis were estimated for both egg weights and for rate of growth relative to the remaining expected growth (R). R was smaller for dwarf hens which thus reached mature egg weight later than normal females. Significant heterosis was found for both egg weights of dwarf hens (12.6 and 9.5%) and for the initial egg weight of normal ones (12.8%). Negative heterosis for R obtained for dwarf hens (-17.4%) corresponded to a flatter egg weight curve of these crossbreds., On a ajusté individuellement une courbe de la forme W = P - Q exp (-Rt) au poids de l’oeuf (W) mesuré entre 18 et 51 sem d’âge (t) chez des poules de 8 groupes génétiques définis par la combinaison du génotype au locus de nanisme lié au sexe (normal ou nain) et de la lignée (souche Leghorn blanche, souche à oeuf brun et leurs croisements réciproques). L’écart type résiduel après ajustement de la courbe exponentielle aux valeurs de poids, évalué pour chaque groupe par la méthode des moindres carrés, a pris des valeurs comprises entre 1,27 et 1,74 g. De la même manière, les estimations des poids du premier oeuf (P - Q) étaient situées entre 26,9 et 33,9 g, et celles de l’oeuf à maturité entre 56,9 et 65,8 g d’autre part. On a évalué les effets du génotype et de la lignée et l’hétérosis pour les poids de l’oeuf et l’accroissement de poids journalier relativement au poids restant à gagner (R). La valeur de R était inférieure pour les poules naines qui par conséquent ont atteint leur poids d’oeuf à maturité plus tard que les femelles normales. Un hétérosis significatif a été obtenu pour les 2 poids d’oeuf chez les poules naines (12,6 et 9,5%) et pour le poids du premier oeuf chez les femelles normales (12,8%). L’hétérosis négatif trouvé pour R chez les poules naines (-17,4%) correspond au fait que la courbe du poids de l’ceuf étaient plus aplatie chez les croisés de ce génotype.

Eleven sun-cured hays with a crude protein and neutral-detergent fibre content ranging from 57 to 207 and 428 to 744 g/kg DM, respectively, were studied for the relationship between in vivo DM digestibility (DMD) in sheep and estimates from chemical composition (crude protein content and Van Soest fibre analyses), in vitro Tilley and Terry technique, enzymatic (digestion by cellulase, pepsin + cellulase and neutral-detergent + cellulase), and in situ techniques (potential and effective degradabilities). On the basis of the coefficient of determination (R2) and the residual standard deviation (RSD) of the regression of in vivo DMD on the results from the investigated techniques, the neutral-detergent + cellulase method (R2 = 0.979; RSD = 0.993) and the in situ (effective degradability; R2 = 0.968; RSD = 1.21) methods gave the most precise estimate of DMD. While the in situ technique requires fistulated animals and is difficult to standardize, the neutral-detergent + cellulase method is simple and rapid, and standardized cellulases are commercially available, so that this technique could provide a suitable tool for predicting DMD of hay.

A study utilizing 91 barley samples was conducted to investigate the feasibility of predicting the nitrogen-corrected true metabolizable energy (TMEn) value of barley for poultry. The samples included three barley types (six-row malting and feed and two-row feed) that were grown at 12 different locations in Manitoba from 1986 to 1988. Substantial variation in chemical and physical measurements was evident among the barley samples studied, and data indicated TMEn of 13.10–14.59 with a mean of 13.97 (MJ kg−1 on dry matter). It was found that TMEn was negatively correlated with neutral detergent fibre (NDF) level (r = −0.784) and positively correlated with test weight (r = 0.627). Significant relationships also existed between TMEn and contents of starch and fat, but only 7–8% of the variation in TMEn was accounted for by those variables. Inclusion of NDF, gross energy (GE), fat, protein and starch terms in a multiple-regression equation resulted in a slight decrease in residual standard deviation and an increase in R2. The best combinations of predictor variables were, in decreasing order, NDF, GE and fat selected from stepwise regression and NDF, fat and starch selected from the Mallows Cp values. With the exception of the equation using test weight as sole predictor, the errors estimated from the simple- (NDF as sole predictor), multiple- and stepwise-regression equations or the equation selected according to the Mallows Cp value were similar to the error approached for the biological measure, TMEn. Key words: Rooster, barley, nitrogen-corrected true metabolizable energy, prediction

[EN] Thirty-one mixed diets for rabbits (DE concentration from 8.44 to 12.29 MJ/kg) were used to predict the digestibility of dry matter (dDM), organic matter (dOM), gross energy (dGE) and digestible energy concentration (DE) from some in vitro gas production parameters, using frozen caecal content from rabbits. Step-wise multiple regression analysis showed that the most significant contribution to the variation expressed by dDM, dOM, dGE and DE arises from crude fibre content (CF). Multiple regression analysis considered more than one independent variable, but it gave only marginally improvements in terms of the accuracy of digestibility prediction. The best equations in terms of R2 and residual standard deviation (RSD) values were: DE (MJ/kg DM) = 0.75 - 0.291 CF - 0.208 ADL + 0.856 GE (R2 = 0.895, RSD 0.279) and dOM (%) = 91.8 - 1.756 CF - 1.283 ADL (R2 = 0.849, RSD 1.655) where CF = crude fibre (%DM), ADL = acid detergent lignin (%DM), GE = gross energy (MJ/kg DM). Dry matter loss (DMl, %) was the in vitro gas production parameter which correlated most closely with dDM, dOM, dGE and DE. The best prediction equations were: DE (MJ/kg DM) = -3.14 + 0.217 DMl + 0.114 B (R2 = 0.734, RSD 0.437) and dOM (%) = - 6.80 + 1.078 DMl + 0.456 B (R2 = 0.691, RSD 2.368), where B is the incubation time (h) at half potential gas. When data of the chemical composition and from fermentation parameters were included concurrently in the model, the most significant contribution to the variation explained of dDM, dOM, dGE and DE still arose from CF. These results suggest that in vitro gas production could be an interesting method of predicting the nutritive value of rabbit diets, but further investigations are required to increase caecal inoculum standardisation and its prediction ability.

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.

Data from 400 cow carcasses were used to develop a new quality grading system. First principal component (FPC) values were determined for shear force (SHR) and palatability attributes of tenderness, connective tissue amount, flavor, and juiciness (JUC). Associated eigenvector load values for those traits were -.48, .54, .51, .46, and .05, respectively, and FPC values ranged from -3.20 to 2.18. Linear regression, using FPC as the independent variate, and SHR and palatability attributes (8-point scale, where 8 was highest) as dependent variates explained a significant amount of variation (P less than .001) in all traits, except JUC, and resulted in R2 values of .71, .89, .80, .66, and .01, respectively. A predictive quality-grade equation was developed using FPC as the dependent variate, and overall maturity (OM), lean color, marbling score (MS), lean firmness, lean texture, fat color (FC), and marbling fineness ([marbling texture + marbling distribution]/2; 8-point scale, where 8 was highest) as independent variates. The resulting best-fit prediction equation (FPC = -.052-[.0031 x OM] + [.0013 x MS] + [.31 x FC]) explained a significant amount (P less than .001) of variation in FPC with R2 = .53, Cp = 15.0, and residual standard deviation = .69. A short-cut equation was developed from this prediction equation. Simple correlations for OM, MS, and FC with FPC were -.56, .39, and .64, respectively. Cow carcasses were assigned to one of three quality grades based on FPC values that corresponded with predetermined acceptability levels for each palatability trait. Newly developed grades were quantitative and less variable than existing grades. Future grades for cow carcasses should include fat color to predict palatability.

Relationships between organic matter digestibility (OMD, %) and a range of laboratory measurements have been developed on 43 samples of whole maize plants in trial 1, in 1967 and 1968 and on 118 samples in trial 2, in 1987 and 1988 from three locations in France. Laboratory measurements included two chemical techniques (Weende crude fibre and van Soest methods), one biological technique (in vitro digestibility) and one enzymatic technique (pepsin-cellulase method). Age after flowering and dry matter content were the two parameters best correlated with OMD. The residual standard deviation (RSD) of the prediction was 1.54, taking into account a significant interaction of location on the slope for age and a location of year effect on the interception of the Y axis. As age is not always known in laboratory analysis, it was not included in the predicting equation. All the laboratory techniques allowed the same prediction (RSD about 2 points for 118 samples) but the relationships were significantly affected by year and location. In contrast, OMD is closely correlated with indigestible cell wall content (RSD = 0.53 for 55 samples studied at Clermont-Ferrand). It is concluded that OMD could be better predicted by methods based on in vitro digestibility or pepsin-cellulase digestibility for traditional forages than for whole plant maize.

Carcasses, with an average weight of 80.8 kg, from 130 Lacombe and 96 Yorkshire pigs were probed for fat and muscle depth at the carcass grade site using the Hennessy Grading Probe (HGP). Similar data were collected by the Aloka SSD-210DXII Echo Camera (AEC) at the carcass grade site and at the Canadian Swine Improvement Program loin and mid-back sites. Muscle-width and muscle-area measurements were also made with the AEC. The R2 (residual standard deviation: RSD), using the HGP, for prediction of carcass lean yield at the grade site using fat and muscle depth was 0.58 (16.4). When fat and muscle depth were measured by the AEC, the R2 (RSD) at the mid-back, loin and grade sites to predict carcass lean yield was 0.41 (19.7), 0.47 (18.5), and 0.53 (17.5), respectively. When the AEC muscle-depth measurements were replaced with muscle-area measurements, the R2 increased (RSD decreased) at the mid-back, loin and grade sites to predict carcass lean yield (0.60 (16.0), 0.64 (15.2), and 0.59 (16.4), respectively). Real-time ultrasound (AEC) provides precision for the prediction of carcass lean content similar to that of the electronic grade probe (HGP). However, combining fat- and muscle-depth measurements made by the HGP with muscle-area measurements made by the AEC at the loin increased the R2 from 58 to 66% for the prediction of carcass lean yield. The suitability of the use of real-time ultrasound as an alternative to electronic grading probes, at this time, appears to be limited. Key words: Swine, real-time ultrasound, carcass grading

An analysis of data from 102 energy balance trials carried out with Granadina goats, 32 with castrated males and 70 with lactating females, was made with the aim of establishing relationships between methane production and some nutritive attributes of the diet. The diets were based on pelleted lucerne (Medicago sativa) hay and barley and differed widely in the amounts and proportions of their ingredients. Methane production was measured by open-circuit respirometry. The between-animal variation in CH4 production was found to be +/- 8%. In absolute terms, CH4 production increased on increasing the intake of energy. The CH4 loss was 6.56 or 9.75 kJ/100 kJ of gross energy or digestible energy intake, respectively. The prediction of the CH4 production was best described from knowledge of the amounts of the "in vitro" digestible fractions (g/d) of both the neutral-detergent solubles (DNDS) and the neutral-detergent fibre (DNDF), according to the equation CH4 = 2.24 + 0.0299 DNDS + 0.0889 DNDF. The regression was highly significant and the residual standard deviation +/- 6.63 or +/- 20% of the mean. It was also found that on increasing the feeding level by one multiple of maintenance there was a reduction in CH4 losses of 1.51 kJ or 2.19 kJ/100 kJ of gross energy or digestible energy intake, respectively. The CH4 production (kJ/100 kJ of gross energy intake) was also related to the apparent digestibility of energy (D, %) determined at the level of nutrition close to maintenance. The equation was CH4 = -2.58 + 0.151 D. The regression was highly significant statistically.

Os objetivos neste estudo foram, a partir da forma generalizada da função de Michaelis-Menten, derivar novas funções, acumulada e diária, para estimar a produção de leite, em 305 dias de lactação, de vacas da raça Gir, usando informações de produção de leite no dia do controle leiteiro. Foram utilizadas 7.412 lactações de 3.416 vacas da raça Gir com partos entre 1987 e 2004, distribuídos em 51 rebanhos participantes do Programa Nacional de Melhoramento Genético do Gir Leiteiro, coordenado pela Embrapa Gado de Leite e pela Associação Brasileira dos Criadores de Gir Leiteiro. Foram ajustados os modelos para as subclasses nível de produção (Alto > 2.628 kg de leite na lactação; Baixo 5). Os valores do desvio-padrão residual variaram de 0,131 a 0,984, os do Coeficiente de Determinação (R²) foram próximos de 1 e de Durbin-Watsom foram próximos de 2. A estatística de Theil (U) apresentou valores de U M e U V próximos de 0 e de U C próximos de 1, sugerindo acurácia das estimativas. Estes resultados indicam bom ajuste da função. A persistência da lactação relaciona-se ao parâmetro d e o tempo de ocorrência do pico da lactação se relaciona simultaneamente com os parâmetros d e n. Então, as novas funções ajustam-se bem a lactações de vacas da raça Gir e são caracterizadas pela simplicidade no procedimento de ajuste, pela facilidade de interpretação biológica dos parâmetros e pela flexibilidade, ou seja, é possível obter a função acumulada a partir da função diária. As novas funções apresentam-se como funções alternativas para projetar lactações não encerradas e podem ser utilizadas na avaliação genética de animais em programas de melhoramento genético. This study aimed to calculate new accumulated and daily functions based on the Michaelis-Menten equation to estimate the 305-days production of Gir cows using test day milk yields. Data consisted of 7,412 lactation records of 3,416 Gir cows (Bos indicus) collected from 1987 to 2004 in 51 herds assisted by the National Breeding Program of the Dairy Gir, coordinated by the Dairy Cattle National Research Center (EMBRAPA) and the Brazilian Association of Dairy Gir Breeders (ABCGIL). The models included the effects of herd milk production (High and Low), calving season (dry e rainy) and parity order (OP 1 to 5 and > 5). Residual Standard Deviation values ranged from 0.131 to 0.z984. Estimates for the Coefficient of Determination (R²) were close to one and for the Durbin-Watson coefficient were close to two. Values of statistics of Theil (U) were close to zero for U M and U V and close to one for U C suggesting a good fitting for the function. The persistency of milk yield is related to the parameter d and the time of the pick yield is related to both parameters d and n. The new functions are characterized by simplicity of the fitting procedure, biological interpretability of the parameters and flexibility to convert the daily function to the accumulated function. The new functions are an alternative to project lactations with records in progress to estimate the production required in genetic evaluation systems based on 305-day milk production records.

The present study summarizes a calibration study in which 104 samples, representing reliable and diversified data from all over the world selected for reliability, were analyzed for in vitro digestibility in three different laboratories. Special attention was given to the following topics: the variability between laboratories and methods of calibration between laboratories; contribution of standards; and source of error for both in vitro and in vivo data simultaneously. Correlation between the in vitro sets of the different laboratories was high (r2 from 0.931 to 0.975; residual standard deviation (RSD) from 0.037 to 0.025). Correlation between laboratories and in vivo data was also high, with similar r2 and RSD for dry matter digestibility and somewhat lower for organic matter digestibility. Indirect calibration of each laboratory on in vivo DMD data by its regression on one of the other laboratories instead of directly on in vivo data, reduced r2 by no more than 0.0009–0.0012. This was despite the fact that each laboratory had a different regression equation on the same in vivo data. Correction of in vitro values by means of a run factor based on three reference feeds with digestibility of 0.5, 0.6 and 0.8 did not improve the calibration. Correlating the residuals from the regression analyses of in vivo on in vitro data for pairs of similar feeds in the same run (one serving as a ‘standard’ for the other) resulted in very low and mostly insignificant r values. This indicates that in two similar feeds, the deviations of the in vitro digestibilities from their respective in vivo digestibilities were not consistent. One must therefore conclude that the inclusion of many standards of this nature does not improve the accuracy of the digestibility estimates. A few constant well spaced standards can serve as a check for large deviations that indicate something went wrong in a given run. A different calibration method using a functional relationship model instead of a regression model was also tested. It was found to be appropriate for in vivo/in vitro calibration because it not only takes into account errors of observation in both variables, but also calculates the relationship between in vivo (OMD) and in vitro data from all the laboratories simultaneously.

The total and individual trimmed meat yield of six hind quarter cuts and one fore quarter cut were estimated for 200 carcasses from animals of mixed origin with regard to breed, sex and feeding regimen. The linear models included carcass weight and visual assessment of fatness and conformation by means of seven fat and five conformation classes. Amount of variation accounted for (R2) was the most favourable for total yield (87.3%) and the least favourable for the rib-eye cut (43.5%). Carcass weight contributed to most of the variation accounted for and had a positive effect on the yield for all the cuts. Fat score and conformation score were significant in the models of all cuts and total yield except for the rump, and contributed at least 10 percentage points to the R2-value for the topside, thick flank, loin, rib-eye and fillet. Fat score had a negative effect on trimmed yield of all cuts except for the loin, which could be attributed to less trimming compared to other cuts. Conformation score had positive effects on the yield of all cuts except for the rump (non significant), fillet and thick flank. Fat score had the largest proportional effect (to size of the cut) on topside and thick flank, which both contain significant fat deposits. Conformation score had the largest proportional effect on the loin and rib-eye, which is probably due to the synergistic effect of fatness and conformation on the conformation score in this region. The accuracy of estimation of total yield for the purpose of awarding premiums to yield categories was considered to be good when the residual standard deviation of the estimated yield was compared with the standard deviation of the trial sample. However, separating cuts into different weight categories was less accurate and varied among cuts, which suggests that more information is needed for more reliable models before accurate individual yields of cuts can be predicted in practice. Keywords : Conformation score; fat score; premiums; saleable meat South African Journal of Animal Sciences Vol. 35 (3) 2005: pp.195-205

Foram utilizadas 48 fêmeas suínas abatidas aos 50, 60, 70, 80, 90, 100, 110 e 120 kg para determinar os rendimentos de cortes comerciais e de carne magra na carcaça, visando-se estimar área de olho de lombo (AOL) e espessura de toucinho (ET), utilizando-se a técnica de ultra-sonografia em tempo real. Foram tomadas medidas ultra-sônicas in vivo, para AOL e ET; nas carcaças quentes, utilizou-se equipamento óptico da marca Hennessy, a fim de estimar a ET e profundidade de músculo (PM). As equações para estimar a quantidade de músculo na carcaça obtiveram R2 entre 0,97 e 0,99, com desvio-padrão residual entre 0,27 e 0,73. Os valores de R2 para área de olho de lombo, por ultra-som e pela medida direta nas carcaças, foram de 0,97 e 0,98, respectivamente, com desvio-padrão residual para as duas estimativas de 3,07 e 3,13. Os ajustes das equações para espessura de toucinho por ultra-som, espessura de toucinho óptico e espessura de toucinho medida diretamente na carcaça apresentaram R2 de 0,99; 0,82; e 0,86, respectivamente, com desvio-padrão residual de 2,39; 3,13; e 2,91. As equações obtidas por regressões lineares e múltiplas, a partir dos dados da dissecação, ajustaram equações precisas para estimar rendimento de cortes comerciais e de carne magra nas carcaças de suínos entre 50 e 120 kg de peso corporal. A técnica de ultra-sonografia em tempo real é acurada para se estimarem a área de olho de lombo e a espessura de toucinho de suínos in vivo. Forty-eight female pigs slaughter at 50, 60, 70, 80, 90, 100, 110 and 120 kg were used to determine commercial cut and of lean meat yields in pork carcass, with a view of the Longissimus muscle area and backfat thickness estimation by using the real-time ultrasound technology. Live ultrasonic measurements were taken for Longissimus muscle area and for backfat thickness and measures in the hot carcass were made, using optical Henessy equipment, to estimate the backfat thickness and Longissimus muscle depth. The regression equations to estimate the amount of muscle in the carcass obtained a R2 between 0.97 and 0.99, with a residual standard deviation (RSD) between 0.27 and 0.73. The R2 values for Longissimus muscle area obtained by ultrasound and for backfat thickness direct in the carcass, were 0.97 and 0.98, respectively, with a RSD of 3.07 and 3.13. The adjustment of the regression equations for ultrasound backfat tickness, for optical backfat tickness and for directly carcass backfat tickness measurement, presented R2 of 0.99, 0.82, and 0.86, respectively, with a RSD of 2.39, 3.13, and 2.91. The fitted regression equations obtained by simple and multiple linear regressions from the dissection data set were precise to estimate commercial cuts and of lean meat of pork carcasses between 50 and 120 kg. The real-time ultrasonograph technology is accurate to estimate the Longissimus muscle area and backfat tickness in live animal.

We evaluated 20 slaughtered cattle with ultrasound before hide removal to predict fat thickness and ribeye area at the 12th rib for possible use in carcass composition prediction. Carcasses were fabricated into boneless subprimals that were trimmed progressively from 2.54 to 1.27 to .64 cm maximum fat trim levels. Stepwise regression was used to indicate the relative importance of variables in a model designed to estimate the percentage of boneless subprimals from the carcass at different external fat trim levels. Variables included those obtained on the slaughter floor (ultrasound fat thickness and ribeye area; estimated percentage of kidney, pelvic, and heart [KPH] fat; and warm carcass weight) and those obtained from carcasses following 24 h in the chill cooler (actual fat thickness, actual ribeye area, estimated percentage of KPH fat, warm carcass weight, and marbling score). At all different subprimal trim levels, percentage KPH was the first variable to enter the model. In the models using measures taken on the slaughter floor, ultrasound fat thickness was the only other variable to enter the model. Ultrasound fat thickness increased R 2 and decreased residual standard deviation (RSD) in models predicting subprimals at 2.54-cm maximum fat trim; however, at 1.27- and .64-cm trim levels, R 2 and RSD increased. Models using the same two variables (except actual fat instead of ultrasound) in the cooler were similar to those using data from the slaughter floor. However, as more cooler measurement variables entered the models, R 2 increased and RSD decreased, explaining a greater amount of the variation in the equation. Ultrasonic evaluation on the slaughter floor may be of limited application compared with the greater accuracy found in chilled carcass assessment.

To investigate the effects of orientation of beef ribs in an electromagnetic field and storage time on the prediction of rib composition, 64 ribs (IMPS 103) were scanned (2.5 MHz) in three different orientations: posterior end first (POS), dorsal end first (DOR), and fat side first, blade end down (BLD). Scans were obtained after 1 and 5 d of storage. Scan peak, rib weight, 3/4 fat depth, length, and temperature were used to predict percentage and weight of dissectable and fat-free lean. The mean peak for the BLD scans was four to six times higher than the POS or DOR scans. Ribs scanned in the BLD orientation on d 5 had the highest coefficient of determination (CD; 94.0) and the lowest residual standard deviation (RSD; .22 kg) for prediction of lean weight. The POS and DOR scans were similar at d 5 (CD = 91.4 and 90.3, respectively) with RSD of .30 and .31 kg, respectively. Prediction of lean percentage resulted in lower CD than predicting lean weight. For percentage of lean, BLD scans at d 5 once again resulted in the best CD (81.6) and lowest RSD (1.5%). Prediction of percentage lean using POS or DOR orientation resulted in CD of 71.2 and 67.1 and RSD of 2.0 and 2.1%, respectively. The CD were similar for POS and DOR scans at d 1 and d 5. Predicting fat-free lean weight resulted in equal or lower CD than dissected lean and higher CD for lean percentage. It seems that orientation during scanning, when consistent, is not a major concern.(ABSTRACT TRUNCATED AT 250 WORDS)

Median weaning weight and its relationship with the median mature liveweight of does was quantified for four commercial Australian cashmere farms in various parts of Australia. Individual liveweights, of does of all ages at the time of weaning in December, ranged from 9 to 61 kg. Individual farm means of adult does (>1 year old) ranged from 24.6 to 38.8 kg. The model for the logarithm of liveweight was: log10(liveweight) = a + br(Age + 0.6); where a, b and r are parameters that are different for each farm. The result that the r parameter differs with farm was statistically significant (P = 9.4 × 10−6). The percentage variance accounted for was 84.4% and the residual standard deviation was 0.042. Farms differed greatly in the median mature liveweight with some farms reaching ~44 kg and others only 31 kg. Median weaning weight was 14.1 kg (range 11.4–16.8 kg). Median weaning weight as a percentage of median mature doe liveweight on a particular farm varied from 32 to 42%. These weaning weights appear low in absolute and relative terms and thus are likely to incur production penalties.

Young bulls were ultrasonically scanned to 1) study breed differences for 12th rib fat depth (USFAT) and longissimus muscle area (USREA), 2) evaluate the nature and predictability of USFAT and USREA development, and 3) determine the effect of age (YFAT and YREA, respectively) or weight (WREA) adjustments on USFAT and USREA. Angus (AA), Charolais (CH), Hereford (HH), Shorthorn (SS), and Simmental (SM) bulls (n = 886) were studied in 2 yr at two stations. Breed differences were observed for end of test USFAT (AA = SSHHCH = SM, P.05) and USREA (CH = SMSS = AAHH, P.05). Within-breed coefficients of variation were 29.8 to 48.4% for end of test USFAT and from 9.0 to 10.8% for end of test USREA. In most bulls, USFAT development was neither linear nor quadratic (P.05), but USREA development was linear (P.05). Prediction equations were characterized by low r2 and high residual standard deviation (RSD) values, although those for d-84 USREA had r2 values from .63 to .70 and RSD values from 4.09 to 5.80 cm2. High associations were obtained between end of test USFAT with YFAT (r = .83 to .91, P.05) and USREA with YREA (r = .74 to .84, P.05) but not for end of test USREA with WREA (r = .31 to .56, P.05). These results indicate that end of test ultrasound measurements may be a useful addition to performance testing programs.

A study was made with 80 samples of 5 grasses and 5 legumes of known dry-matter digestibility in vivo to compare neutral detergent (ND) and acid pepsin (AP) pretreatments in the technique based on Onozuka SS1500 cellulase for predicting digestibility in vivo. Pretreatment with ND removed a mean 37.2% of the dry matter compared with 30.4% by AP. The quantity of dry matter solubilized by the cellulase was 25.9 and 25.4% following ND and AP pretreatments. The ND did not appear to change the resistance of the cell wall to cellulase although large quantities of ash were removed. The total proportion of dry matter removed by the ND + cellulase compared with the AP + cellulase was 63.1 and 55.8%, respectively. The accuracy of the two methods for predicting digestibility in vivo was determined by calculating the residual standard deviation (RSD) for regressions relating digestibility in vivo to the quantity of dry matter solubilized. Pretreatment with ND gave an increased RSD of ± 3.4 percentage units compared with an RSD of ± 2.9 for pretreatment with AP. When the legumes and grasses were considered separately the ND pretreatment increased the RSD for regressions based on grasses (from ± 2.6 to ± 3.3) but slightly decreased the RSD for predicting the digestibility in vivo of legume (from 2.9 to 2.7%). The higher RSD of the regressions based on grasses was associated with the higher analytical error of the ND + cellulase method. This higher analytical error was mainly caused by greater variation in the cellulase half of the analysis following pretreatment with ND. It was concluded that pretreatment with ND increases the dry matter solubilized in the cellulase method, but for grasses any advantage of reducing analytical time is offset by a larger error in predicting digestibility in vivo.

The Hennessy and Chong Fat Depth Indicator and the Ulster Probe automatic recording instruments developed for measuring fat thickness were tested against the optical probe for use in pig carcass classification and grading.Fat thickness measurements were taken using each probe 60 mm from the dorsal mid-line over the m. longissimus at the positions of the 3rd/4th lumbar vertebrae, 3rd/4th last ribs and last rib on a total of 110 hot carcasses covering the range of market weights in Great Britain. The standard deviation of carcass lean proportion at equal carcass weight was 35·4 g/kg.The instruments differed little in the precision of carcass lean proportion prediction: residual standard deviation (g/kg) for the multiple regression with carcass weight and the best individual fat measurement for each probe were: last rib optical probe, 22·1; last rib Ulster Probe, 22·7; and 3rd/4th last rib Fat Depth Indicator, 21/6. Residual standard deviation (g/kg) for carcass lean proportion prediction from carcass weight and all three fat measurements in multiple regression were 21·3 optical probe, 21·3 Ulster Probe and 201 Fat Depth Indicator.Similar mean fat measurements were obtained from the optical probe and Fat Depth Indicator, and for these instruments, but to a lesser extent for the Ulster Probe, the regression relationships with each other and with fat thickness measurements taken on the cut surface of the cold carcass were also similar.The differences recorded in precision are unlikely to be sufficiently important to influence the choice of one probe rather than another.

A function to describe live-weight changes of dairy cows between two calvings is constructed. The following measurements were used: live-weight level (scale), pregnancy parameter, maximum decrease of live weight during the lactation and the time during lactation at which minimum live weight occurred.An analysis was carried out for each of 71 dairy cows of two genotypes, Dutch Friesian and Holstein Friesian crossbreds, given a high or a low concentrate diet. Data set 1 contained data of 40 weeks lactation and data set 2 data from calving to calving.The mean residual standard deviation per genotype-diet group ranged from 11 to 14 kg. The mean R2 ranged over the subgroups in data set 1 from 0·715 to 0·792 and in data set 2 from 0·857 to 0·906. Diet had a significant influence on the magnitude of the decrease in live weight during the lactation (71 v. 46 kg) and the mean time during the lactation at which minimum live weight occurred. The latter time ranged from day 58 to 100 over the four groups and a diet effect existed. The pregnancy parameter could not be estimated accurately based on measurements during the first 40 weeks of lactation. The correlation coefficient between the pregnancy parameter in data sets 1 and 2 ranged from +0·13 to +0·78. The other parameters showed coefficients from +0·82 to +0·99.

A study was made of the bias (non-random error) of regressions for predicting crude protein (CP) content, dry matter digestibility (DMD) in vivo and voluntary intake (VI) of forage from its near-infrared reflectance (NIRR) properties measured at 19 wavelengths. The regression was derived from 97 samples including six tropical grasses, leaf and stem fractions, pelleted and chopped forms and regrowths varying from 28 to 150 days. Bias associated with differences in feed type was proved to exist in regression equations for predicting nutritive value when it was shown that the residual errors of these regressions were significantly reduced by including the combined effects of grass species, fraction, form and age. Measures of the bias which might be expected were calculated for two contrasting situations. The internal measure of bias (IMB) was ‘estimated’ as a possible average bias of a feed type when predicting nutritive value for samples of similar types to those used when deriving the calibration equation, while the extrapolation measure of bias (EMB) was ‘estimated’ as that associated with prediction of the nutritive value for feed types not included in the calibration equation. Crude protein was estimated with a residual standard deviation (RSD) of ± 0.77% when regressed against the 19 reflectance values, but when information on grass species, parts and regrowth was also included in the regression the RSD was reduced to ± 0.46%. The maximum difference in the IMB between grass species was 0.80% crude protein, while for EMB it was 3.35% crude protein. When separated leaf and stem were considered the differences were larger. Dry matter digestibility was estimated with an RSD of ± 2.7% which decreased to ± 1.8% when grass species, age, form and study were taken into account. Grass species had only a small effect on IMB but the maximum difference for EMB between species reached 5.6%. The EMB indicated that the largest differences between deviations were associated with age of regrowth but this effect was absent with the IMB. Voluntary intake was estimated with an RSD of ± 7.2 g/kg W 0.75 /day, which decreased to ± 2.5 g/kg W 0.75 /day when grass species, form, part and age were taken into account. The largest bias was for pelleted and chopped forage with mean differences of 14.5 for IMB and 28.8 g/kg W 0.75 /day for EMB, respectively. It was concluded that when nutritive value is predicted by NIRR the estimates may often be biased according to the grass species, plant part or physical form of the sample tested and that the pattern of bias may be complicated. In order to obtain accurate predictions from NIRR full account must be taken of this bias. This might be achieved by correcting the nutritive value estimates made from an NIRR regression, based on a large number of feed types, by addition of the bias associated with the feed type being estimated.

SummaryIndividual muscles, individual bones and fat depots from the non-carcass parts (head, foreshanks and tail) of 40 steers (chilled carcass weight 142–174 kg) were studied in an attempt to predict carcass composition more accurately than in an earlier study where the crude non-carcass parts, head, tongue, foreshanks and hindshanks were used.The technique did not improve the accuracy of prediction of muscle or bone. Carcass fat, which was calculated by difference in the earlier study, was predicted more accurately using intermandibular fat weight (requiring 3–4 min to obtain) plus fat thickness at the 12th rib. However, the accuracy of this prediction (carcass fatness range 14·0–33·6%, residual standard deviation 1·98%) was no greater than that using chilled side weight plus fat thickness at the 12th rib.Careful dissection and trimming of anatomical entities from the non-carcass parts therefore offered a slight advantage only in the prediction of carcass components over existing techniques.Substitution of hot side weight for chilled side weight, and sacral crest fat thickness for fat thickness at the 12th rib did not lead to a loss in the accuracy of prediction of muscle, bone or fat.

From data reported by authors who had serially determined the chemical composition of the empty body of growing lambs of a wide range of genotypes, the energy density of empty body gains (E/G) at intervals from 20 to 50 kg empty body weight (EBW) were estimated. Energy density of empty body gain was related to the EBW of these lambs and to an approximation of the mature weight for the genotype (MW) obtained from a separate data set. The addition of MW to the function relating E/G to EBW increased the r2 from .793 to .940 and reduced the residual standard deviation from .550 to .300 Mcal/kg. Combining EBW and MW as predictors of E/G provided the following function: E/G = 5.718 + .093 (EBW) - .036 (MW). Energy density of empty body gain was approximately constant across all genotypes when comparisons were made at a constant EBW/MW. Thus, both EBW and mature weight of the genotype are closely related to E/G and should be considered important for accurately predicting net energy of gain requirements for growing lambs.

1. An examination of carcass reports from two groups of bacon pigs (comprising eleven litters and seventy-five pigs) was made to determine whether the variation in growth rate of normally reared pigs was associated with differences in carcass measurements. The method adopted was to fit orthogonal polynomials to the third degree to the last 10–11 fortnightly weighings converted to logarithms.2. The residual standard deviation after fitting was about 2–4%. For present purposes, the fitting of second degree parabolae appeared adequate.

1. A mixed sward was divided into two areas receiving high (H) or low (L) levels of fertilizer nitrogen, and on two occasions, in spring and summer, herbage cut from each area was given to three steers in a continuous digestibility trial lasting 45 days. Digestibility and faecal nitrogen concentration were measured for each animal for each 24 hr. period, and equations relating these variables were calculated for each trial from the unit 24 hr. observations and from averages over three animals and over successive 2-, 3-, 5- and 9-day periods.2. In the two spring trials herbage digestibility remained constant for the first 12 days and then declined by about 0·4 units per day. In the summer trials digestibility declined throughout by 0—25 units per day. The nitrogen content of the herbage was consistently 25% higher for treatment H, and declined steadily throughout each trial.3. Both season of cutting and fertilizer treatment had a significant effect on the faecal index regressions obtained. Equations calculated from average values for three animals and 3 days predicted digestibility coefficients from 3% faecal nitrogen as follows: equation (1) spring H, 76·5; (2) spring L, 77·5; (3)summer H, 71·8; (4) summer L, 72·8.4. The joint residual standard deviation for equations (1) to (4) had the low value of ± 0·90 units of digestibility, indicating that ‘single-sward’ regressions are considerably more accurate than those embracing a great variety of herbages. Even greater accuracy was obtained when digestibility and faecal nitrogen concentrations were measured for periods longer than 3 days; in general, residual variance varied inversely with period length. Using values for individual animals in the regressions increased the residual variance slightly, but because differences between animals in the digestibility-faecal nitrogen relationship were small and generally nonsignificant, the increase was by a factor of the order of 1·6, not 3.5. Standard errors of prediction were calculated, although there is evidence to suggest that these underestimate the error of digestibility coefficients predicted for free-grazing animals. It is concluded that faecal index regressions are accurate enough for measuring digestibility per se in grazing experiments. In the measurement of herbage consumption, however, the experimental error is likely to be high, partly because indigestibility is the factor required to be predicted, but mainly because of large differences in appetite among similarly treated animals.

Deuterium oxide dilution space (DS) predicted by a 1 or 2 compartment kinetic model was used to estimate total body water in male kids. Empty body water (EBW), total body water at slaughter (TBW) and total body water calculated in the middle of day of injection (TBWM) were predicted with more accuracy by 2 compartment models. Residual standard deviation for EBW, TBW and TBWM estimated from a 2 open compartment model was 939 g, 464 g and 450 g respectively. Measurement of DS provides an accurate method to determine body water content and body composition.

The growth curves of 280 “Nero di Parma” pigs from birth to maturity were calculated by applying nine different models (regressions from 1st to 4th degree and nonlinear regressions following the Brody, Logistic, Janoschek, Bertalannfy and Gompertz models) to 1109 individual records of body weight (BW) from two different data sets. The goodness of fit of experimental data was calculated by means of Residual Variance, Akaike Information Criterion, Residual Standard Deviation and R2. The best fit was obtained by Gompertz equation, as follows: BW(kg)= 240.2±2.4 * esp (-exp (-0.0069±0.0001*(age(d)–213.5±3.1))). Regardless to the model, all correlations between actual and estimated BW were highly significant (P