Individual Animal Observations Research Articles

Nitrogen excretion and utilisation of dairy cows grazing temperate semi-natural grasslands

Diets reliant on grazed, temperate herbage are prone to greater nitrogen (N) losses via urine than balanced stall-fed diets which poses a greater risk for N emissions. Measures for improving the N utilisation in grazing-based dairy cattle systems are predominantly investigated on homogenous clover-ryegrass pastures with high herbage yields and nutritional quality. In contrast, grazing-based systems reliant on less external inputs (e.g., synthetic fertilisers or concentrates) using semi-natural grassland as main feed source, such as in large parts of Central Europe, received less attention. The N utilisation and excretion of grazing cows in low-input dairy farms was, thus, investigated on nine commercial organic dairy farms in South Germany across one to four periods per farm. The dataset captured a diverse set of dairy production systems comprising 323 individual animal observations. A mean (± one standard deviation) milk production, DM intake (DMI), and pasture DMI of 23.9 kg (± 5.35), 21.0 kg (± 3.21), and 11.3 kg/d (± 4.83), respectively, was determined using titanium dioxide and faecal crude protein concentration as markers of faecal excretion and diet digestibility, respectively. Milk N use efficiency (MNE; i.e., milk N secretion as share of N intake) averaged 24.7 g/100 g N intake (± 5.91), which is greater than observations in temperate, high-input grazing systems but lower than in cows receiving balanced diets in the barn. The MNE and another seven indicators of N utilisation and excretion displayed a wide range of values. The grazing management factors explaining this variation were, thus, identified via backward elimination. The supplementation strategy had the greatest potential for manipulating N utilisation and excretion of dairy cows. Increasing shares of fresh forages (i.e., meadow grass or clover-grass leys) as well as of hay in supplement DMI increased N utilisation (e.g., MNE) and decreased urinary N excretion (e.g., urinary N to creatinine ratio), while increasing shares of concentrates in supplement DMI are related to lower N losses via urine. At the same time, increases in total supplement DMI reduced N utilisation and increased urinary N excretion. Hence, full-time grazing combined with supplementation of fresh forage and hay in the barn is a viable option for low-input, grazing-based dairy operations with moderate levels of N losses.

On-farm evaluation of models to predict herbage intake of dairy cows grazing temperate semi-natural grasslands

The objective of the present on-farm study was to evaluate the adequacy of existing models in predicting the pasture herbage DM intake (PDMI) of lactating dairy cows grazing semi-natural grasslands. The prediction adequacy of 13 empirical and semi-mechanistic models, which were predominantly developed to represent stall-fed cows or cows grazing high-quality pastures, were evaluated using the mean bias, relative prediction error (RPE), and partitioning of mean square error of prediction, where models with an RPE ≤ 20% were considered adequate. The reference dataset comprised n = 233 individual animal observations from nine commercial farms in South Germany with a mean milk production, DM intake, and PDMI (arithmetic means ± one SD) of 24 kg/d, (±5.6), 21 kg/d (±3.2), and 12 kg/d (±5.1), respectively. Despite their adaptation to grazing conditions, the behaviour-based and semi-mechanistic grazing-based models had the lowest prediction adequacy among the evaluated models. Their underlying empirical equations likely did not fit the grazing and production conditions of low-input farms using semi-natural grasslands for grazing. The semi-mechanistic stall-based model Mertens II with slight modifications achieved the highest and a satisfactory modelling performance (RPE = 13.4%) when evaluated based on the mean observed PDMI, i.e., averaged across animals per farm and period (n = 28). It also allowed for the adequate prediction of PDMI on individual cows (RPE = 18.5%) that were fed < 4.8 kg DM of supplement feed per day. Nevertheless, when used to predict PDMI of individual animals receiving a high supplementation level, the model Mertens II also did not meet the threshold for an acceptable adequacy (RPE = 24.7%). It was concluded that this lack of prediction adequacy for animals receiving greater levels of supplementation was due to a lack of modelling precision, which mainly could be related to inter-animal and methodological limitations such as the lack of individually measured supplement feed intake for some cows. The latter limitation is a trade-off of the on-farm research approach of the present study, which was chosen to represent the range in feed intake of dairy cows across the diverse low-input farming systems using semi-natural grasslands for grazing.

Enteric methane emission models for diverse beef cattle feeding systems in South-east Asia: A meta-analysis

Prediction models for enteric methane (CH4) emissions from beef cattle proposed by various groups may not perform with similar accuracy for the low- and middle-income countries in South-east Asia (SE-Asia) because beef cattle in these countries are raised under different climatic conditions with diverse feeding systems, and have different CH4 emission characteristics. The objectives of this study were to: i) predict CH4 emission (g d−1 animal−1), yield [g kg−1 dry matter intake; DMI)−1], intensity [g kg−1 average daily gain)−1], and CH4 conversion factor (Ym) using an intercountry database of individual animal records from SE-Asia; ii) evaluate the impact of different dietary forage contents (all-, high- and low-forage) representing the diverse feeding systems on CH4 emission, yield, intensity and Ym in SE-Asia; and iii) cross-validate equations from this study with published data. A total of 398 individual animal observations of beef cattle from SE-Asia were used for this analysis. Linear models developed by incrementally adding covariates revealed that CH4 emission model using only DMI fitted to all data had a root mean square prediction error (RMSPE) of 16.9%. Subsets containing data with 100% forage in the diet (all-forage), 50–85% (high-forage) and < 50% (low-forage) had an RMSPE of 16.5%, 14.7%, and 17.4%, respectively. Linear multiple equation based on DMI and dietary NDF concentration (DMI + NDF_C, RMSPE = 15.2%; all-data) improved prediction accuracy over that of DMI alone. The DMI + NDF_C models for all-forage (RMSPE = 14.6%) and high-forage subsets (RMSPE = 13.3%) except for low-forage (RMSPE = 16.4%), improved the precision and accuracy of CH4 emission prediction. Methane yield and CH4 emission intensity could not be reliably modelled with the current database. The present study provides improved CH4 prediction models for beef cattle managed under diverse feeding systems in SE-Asia and affirmed that region-specific models are needed to reliably predict beef cattle CH4 emission at national or regional levels, particularly for low- and middle-income countries.

PSX-23 Predicting feedstuff associative effects in goats

Abstract The objective was to develop means of predicting effects of supplementation on forage and total ME intake in goats. A database of treatment mean observations from the literature currently has 529 individual animal observations with ad libitum intake from 24 studies. Essential data were supplement and forage OM digestibility without supplementation or sufficient information for estimation, total diet OM digestibility, forage and supplement CP concentration and OM intake, and BW. Intake of ME was assumed 15.104 MJ/kg digested OM. For observations with supplementation, forage ME intake was the difference between total and supplement ME intake. The original database had 134 observations, although 24 were omitted because of questionable values and(or) unlikely estimates. Forage averaged 9.4 MJ/kg DM of ME (SD=1.77, 4.6–12.6), 59.1% OM digestibility without supplementation (SD=8.67, 42.9–77.1; AOMDIGFOR), and 8.1% CP (SD=3.72, 2.1–17.1) (PTCPFOR), and supplement was 12.3 MJ/kg ME (SD=1.38, 7.1–15.1) and 28.4% CP (SD=41.5, 0–288). Forage ME intake (MEIMBWFOR, kJ/kg BW0.75) was regressed against supplement ME intake (MEIMBWSUP, kJ/kg BW0.75), with inclusion of other variables tested. The equation explaining most variation (R2=0.77) was 155.9 ± 135.75 – (0.5403±0.04381 × MEIMBWSUP) – (41.0063±20.71841 × PTCPFOR) + (5.3497±2.31608 × AOMDIGFOR) + (0.74056±0.345002 × PTCPFOR × AOMDIGFOR), with P values of 0.253, &amp;lt; 0.001, 0.050, 0.023, and 0.034, respectively. The mean difference between observed and predicted MEIMBWFOR (379±15.0 and 382±12.8, respectively) was -3±7.4, with minimum and maximum values of -181 and 167, respectively. The equation for the regression of residual MEIMBWFOR (observed – predicted) against predicted values was -10±22.3 + (0.018±0.0553 × predicted MEIMBWFOR), with the intercept and slope not different from 0 (P = 0.670 and 0.739, respectively). Equations describing associative effects can be used to predict the quantity of a particular supplement with a given forage necessary to meet nutrient requirements of goats in specific production settings.

Development of a dynamic energy-partitioning model for enteric methane emissions and milk production in goats using energy balance data from indirect calorimetry studies

The main objective of this study was to develop a dynamic energy balance model for dairy goats to describe and quantify energy partitioning between energy used for work (milk) and that lost to the environment. Increasing worldwide concerns regarding livestock contribution to global warming underscore the importance of improving energy efficiency utilization in dairy goats by reducing energy losses in feces, urine and methane (CH4). A dynamic model of CH4 emissions from experimental energy balance data in goats is proposed and parameterized (n = 48 individual animal observations). The model includes DM intake, NDF and lipid content of the diet as explanatory variables for CH4 emissions. An additional data set (n = 122 individual animals) from eight energy balance experiments was used to evaluate the model. The model adequately (root MS prediction error, RMSPE) represented energy in milk (E-milk; RMSPE = 5.6%), heat production (HP; RMSPE = 4.3%) and CH4 emissions (E-CH4; RMSPE = 11.9%). Residual analysis indicated that most of the prediction errors were due to unexplained variations with small mean and slope bias. Some mean bias was detected for HP (1.12%) and E-CH4 (1.27%) but was around zero for E-milk (0.14%). The slope bias was zero for HP (0.01%) and close to zero for E-milk (0.10%) and E-CH4 (0.22%). Random bias was >98% for E-CH4, HP and E-milk, indicating non-systematic errors and that mechanisms in the model are properly represented. As predicted energy increased, the model tended to underpredict E-CH4 and E-milk. The model is a first step toward a mechanistic description of nutrient use by goats and is useful as a research tool for investigating energy partitioning during lactation. The model described in this study could be used as a tool for making enteric CH4 emission inventories for goats.

Predicting enteric methane production from cattle in the tropics

Accurate estimates of methane (CH4) production by cattle in different contexts are essential to developing mitigation strategies in different regions. We aimed to: (i) compile a database of CH4 emissions from Brazilian cattle studies, (ii) evaluate prediction precision and accuracy of extant proposed equations for cattle and (iii) develop specialized equations for predicting CH4 emissions from cattle in tropical conditions. Data of nutrient intake, diet composition and CH4 emissions were compiled from in vivo studies using open-circuit respiratory chambers, SF6 technique or the GreenFeed® system. A final dataset containing intake, diet composition, digestibility and CH4 emissions (677 individual animal observations, 40 treatment means) obtained from 38 studies conducted in Brazil was used. The dataset was divided into three groups: all animals (GEN), lactating dairy cows (LAC) and growing cattle and non-lactating dairy cows (GCNL). A total of 54 prediction equations available in the literature were evaluated. A total of 96 multiple linear models were developed for predicting CH4 production (MJ/day). The predictor variables were DM intake (DMI), gross energy (GE) intake, BW, DMI as proportion of BW, NDF concentration, ether extract (EE) concentration, dietary proportion of concentrate and GE digestibility. Model selection criteria were significance (P < 0.05) and variance inflation factor lower than three for all predictors. Each model performance was evaluated by leave-one-out cross-validation. The Intergovernmental Panel on Climate Change (2006) Tier 2 method performed better for GEN and GCNL than LAC and overpredicted CH4 production for all datasets. Increasing complexity of the newly developed models resulted in greater performance. The GCNL had a greater number of equations with expanded possibilities to correct for diet characteristics such as EE and NDF concentrations and dietary proportion of concentrate. For the LAC dataset, equations based on intake and animal characteristics were developed. The equations developed in the present study can be useful for accurate and precise estimation of CH4 emissions from cattle in tropical conditions. These equations could improve accuracy of greenhouse gas inventories for tropical countries. The results provide a better understanding of the dietary and animal characteristics that influence the production of enteric CH4 in tropical production systems.

202 Repeatability of estrus detection patch readings and relationship with breeding success

Abstract The objective of this study was to assess how well estrus detection patch readings correlated to successful AI breedings and to characterize the within-cow repeatability of estrus detection patch readings across breeding seasons. Data from the Virginia Department of Corrections beef cattle herds were collated for 7 locations over 7 years, with 2 calving seasons per year. Data from Spring of 2011 were missing. The full dataset contained 19,253 individual animal observations, of which 2,389 observations were omitted for failure to report estrus detection patch data. The relationship between estrus patch reading and pregnancy rate to fixed-time AI and subsequent natural service pregnancy rate was assessed by calculating the sensitivity and specificity of patch readings within each location during each breeding season. A true positive was defined as a patch activating and a cow being confirmed pregnant. A true negative was a patch remaining in the non activated state and a cow being confirmed open. A false positive was defined as a patch activating and a cow being confirmed open. A false negative was a patch failing to activate and a cow being confirmed pregnant. Pregnancy rate to AI sensitivity ranged from 0.2 to 1 with a mean of 0.583. Specificity ranged from 0 to 1 with a mean of 0.525. Subsequent natural service pregnancy rate), ranges in sensitivity (0.197 to 1.00, mean 0.563) and specificity (0 to 1, mean 0.545) were similar. Of the 6,249 animals with usable patch data, 934 never presented with an activated patch and 2,064 presented with an activated patch every breeding season. The remaining 3,251 animals averaged presenting with an activated patch 47% of the time and a non-activated patch 46% of the time. Odds of getting pregnant by AI for cows that always flag with the heat patch were 0.415.

192 Effects of dam relative calving date on progeny performance

Abstract The objective of this study was to establish the relationships between when a cow was born within the calving season and the performance of her offspring. Data from the Virginia Department of Corrections beef cattle herds were collated for 7 locations over 7 years (2010 to 2017), with 2 calving seasons per year. Data from Spring of 2011 were missing. The full dataset contained 19,253 individual animal observations. Within each location, year, and calving season combination, the calving season was defined as starting when the first calf was born and terminating when the final calf was born. The relative calving date (RCD) within each calving season was defined by calculating the number of days between when the calving occurred and when the calving season started, divided by the length of the season in days. For heifer calves that were retained as replacement heifers (n = 2,800), the RCD and birth weight of their calves were used as response variables of a mixed-effect model with fixed effects of dam RCD, season (fall vs spring), and calf sex. All 2 and 3 way interactions were also included as fixed effects. Both calving year and sire were used as random effects. Fixed effects were iteratively removed from the model when non-significant; however, non-significant linear terms were retained if involved in a significant interaction term. The final calf RCD model included significant effects of dam RCD (P = 0.006), season (P &amp;lt; 0.001), calf sex (P = 0.0737), and the interaction between dam RCD and calf sex (P = 0.055). The final calf birth weight model included only linear terms for calf sex (P &amp;lt; 0.001) and dam RCD (P = 0.029). Least square means for these relationships are depicted in Table 1.

Parasite survey on a captive wolf population using classical techniques and ELISA coproantigen detection, USA.

As laws change around the United States, wildlife that were once kept as companion animals are now often confiscated by local authorities. They are then euthanized unless a home is found for them at a sanctuary. Wolf sanctuaries are, therefore, becoming increasingly important for their conservation and management. However, little data is available on best practices for the health management of captive wolves, including data on parasitic diseases. Our objective was to assess the prevalence of parasites of captive wolves combining classical coprological techniques and immunoassays based on the detection of coproantigen of selected canid parasites. Fecal samples of 39 animals were collected upon observation of individual animals defecating. All samples were processed using the Fecal Dx® tests, a suite of coproantigen ELISAs for detection of ascarid, hookworm, whipworm, and Giardia (IDEXX Laboratories Inc.). Out of the 39 samples, 38 were processed using the double-centrifugation sugar flotation (DCSF) and 34 using a modification of the Baermann technique. Twenty-eight samples (71.8%) were positive for hookworm, and none positive for the other parasites tested using coproantigen ELISA. Ancylostoma sp. (26, 68.4%), Eucoleus boehmi (13, 34.2%), and Trichuris sp. (2; 5.3%), and Sarcocystis sp. (13, 34.2%) were detected using DCSF. No metastrongyloid lungworm larvae were found. The Cohen's kappa index (0.97) showed excellent agreement between the hookworm coproantigen ELISA and the DCSF using feces preserved in ethanol for a short period of time. This study provides a baseline on the parasites of captive wolves, and shows that recent innovative diagnostics in veterinary parasitology, developed and optimized for dogs, may be used for assessing the health of wolves.

Predicting the response to CTLA-4 blockade by longitudinal noninvasive monitoring of CD8 T cells.

Immunotherapy using checkpoint-blocking antibodies against targets such as CTLA-4 and PD-1 can cure melanoma and non-small cell lung cancer in a subset of patients. The presence of CD8 T cells in the tumor correlates with improved survival. We show that immuno-positron emission tomography (immuno-PET) can visualize tumors by detecting infiltrating lymphocytes and, through longitudinal observation of individual animals, distinguish responding tumors from those that do not respond to therapy. We used 89Zr-labeled PEGylated single-domain antibody fragments (VHHs) specific for CD8 to track the presence of intratumoral CD8+ T cells in the immunotherapy-susceptible B16 melanoma model in response to checkpoint blockade. A 89Zr-labeled PEGylated anti-CD8 VHH detected thymus and secondary lymphoid structures as well as intratumoral CD8 T cells. Animals that responded to CTLA-4 therapy showed a homogeneous distribution of the anti-CD8 PET signal throughout the tumor, whereas more heterogeneous infiltration of CD8 T cells correlated with faster tumor growth and worse responses. To support the validity of these observations, we used two different transplantable breast cancer models, yielding results that conformed with predictions based on the antimelanoma response. It may thus be possible to use immuno-PET and monitor antitumor immune responses as a prognostic tool to predict patient responses to checkpoint therapies.

A network approach to discerning the identities of C. elegans in a free moving population.

The study of C. elegans has led to ground-breaking discoveries in gene-function, neuronal circuits, and physiological responses. Subtle behavioral phenotypes, however, are often difficult to measure reproducibly. We have developed an experimental and computational infrastructure to simultaneously record and analyze the physical characteristics, movement, and social behaviors of dozens of interacting free-moving nematodes. Our algorithm implements a directed acyclic network that reconstructs the complex behavioral trajectories generated by individual C. elegans in a free moving population by chaining hundreds to thousands of short tracks into long contiguous trails. This technique allows for the high-throughput quantification of behavioral characteristics that require long-term observation of individual animals. The graphical interface we developed will enable researchers to uncover, in a reproducible manner, subtle time-dependent behavioral phenotypes that will allow dissection of the molecular mechanisms that give rise to organism-level behavior.

Prediction of portal and hepatic blood flow from intake level data in cattle.

Interest is growing in developing integrated postabsorptive metabolism models for dairy cattle. An integral part of linking a multi-organ postabsorptive model is the prediction of nutrient fluxes between organs, and thus blood flow. The purpose of this paper was to use a multivariate meta-analysis approach to model portal blood flow (PORBF) and hepatic venous blood flow (HEPBF) simultaneously, with evaluation of hepatic arterial blood flow (ARTBF; ARTBF=HEPBF - PORBF) and PORBF/HEPBF (%) as calculated values. The database used to develop equations consisted of 296 individual animal observations (lactating and dry dairy cows and beef cattle) and 55 treatments from 17 studies, and a separate evaluation database consisted of 34 treatment means (lactating dairy cows and beef cattle) from 9 studies obtained from the literature. Both databases had information on dry matter intake (DMI), metabolizable energy intake (MEI), body weight, and a basic description of the diet including crude protein intake and forage proportion of the diet (FP; %). Blood flow (L/h or L/kg of BW0.75/h) and either DMI or MEI (g or MJ/d or g or MJ/kg of BW0.75/d) were examined with linear and quadratic fits. Equations were developed using cow within experiment and experiment as random effects, and blood flow location as a repeated effect. Upon evaluation with the evaluation database, equations based on DMI typically resulted in lower root mean square prediction errors, expressed as a % of the observed mean (rMSPE%) and higher concordance correlation coefficient (CCC) values than equations based on MEI. Quadratic equation terms were frequently nonsignificant, and the quadratic equations did not outperform their linear counterparts. The best performing blood flow equations were PORBF (L/h)=202 (±45.6) + 83.6 (±3.11) × DMI (kg/d) and HEPBF (L/h)=186 (±45.4) + 103.8 (±3.10) × DMI (kg/d), with rMSPE% values of 17.5 and 16.6 and CCC values of 0.93 and 0.94, respectively. The residuals (predicted - observed) for PORBF/HEPBF were significantly related to the forage % of the diet, and thus equations for PORBF and HEPBF based on forage and concentrate DMI were developed: PORBF (L/h)=210 (±51.0) + 82.9 (±6.43) × forage (kg of DM/d) + 82.9 (±6.04) × concentrate (kg of DM/d), and HEPBF (L/h)=184 (±50.6) + 92.6 (±6.28) × forage (kg of DM/d) + 114.2 (±5.88) × concentrate (kg of DM/d), where rMSPE% values were 17.5 and 17.6 and CCC values were 0.93 and 0.94, respectively. Division of DMI into forage and concentrate fractions improved the joint Bayesian information criterion value for PORBF and HEPBF (Bayesian information criterion=6,512 vs. 7,303), as well as slightly improved the rMSPE and CCC for ARTBF and PORBF/HEPBF. This was despite minimal changes in PORBF and HEPBF predictions. Developed equations predicted blood flow well and can easily be used within a postabsorptive model of nutrient metabolism. Results also suggest different sensitivity of PORBF and HEPBF to the composition of DMI, and accounting for this difference resulted in improved ARTBF predictions.

Agarose hydrogel microcompartments for imaging sleep- and wake-like behavior and nervous system development in Caenorhabditis elegans larvae

Caenorhabditis elegans larvae display specific behavior and development that is not observed in adults. For example, larvae go through a molting cycle that includes a sleep-like state prior to the molt. The study of these processes requires high-resolution long-term observation of individual animals. Here we describe a method for simultaneous culture and observation of several individual young C. elegans larvae inside agarose hydrogel-based arrayed microcompartments. We used agarose hydrogel microcompartments to observe and quantify larval specific sleep-wake-like behavior and to observe neuronal rewiring using confocal fluorescence microscopy without acute immobilization. We found no behavioral aberrations caused by area restriction. We show that worms cultured inside hydrogel microcompartments develop into normal adults. Thus, hydrogel microcompartments appear useful for long-term observation of larval behavior and development.

Parity-associated changes in slaughter weight and carcass characteristics of 3⁄4 Charolais crossbred cows kept on a lowland grass/grass silage feeding and management system

AbstractA total of 77 heifers (3⁄4 Charolais crossbred) completed a study which examined the effects of parity number on growth, changes in size of the carcass, carcass composition and meat quality. The experiment had a factorial design based on the factors: parity and level of finish. The parities were: 0 (maiden), 1, 3 and 5. At each parity animals were slaughtered at medium (EU fat class 3) and high (EU fat class 4H) levels of finish. All data were analysed by analysis of variance using individual animal observations, and linear, quadratic and asymptotic trends were explored. Where appropriate, regression equations were derived using individual animal values to describe the relationships between key parameters and the parity status of the finished animal. Animals calved at turn-out to grass in spring (April/May) and the cows were housed at weaning in mid October (mean lactation length of 163 days) and all pregnant animals were given a diet of grass silage without concentrate supplementation during the winter period. The medium level of finish animals were slaughtered 24 days after commencement of the breeding programme for parity 0 or at the end of lactation for the remaining parities, except for parity 1 animals which had a short finishing period. For the high level of finish treatment, parity 0 heifers had a longer finishing period, while all bred animals had a post-weaning finishing period. The high finish animals were finished on a diet of grass silage and concentrates. Birth weights of calves increased until the fourth parity, while weaning weights increased linearly until the fifth parity. Mean daily milk yield increased until the third lactation. Live weight, carcass weight and weights of saleable beef, separable lean and separable fat all followed asymptotic patterns of rapid initial increase and then tended to plateau with increase in parity. The asymptote values for live weight and carcass weight were 723·0 and 383·2 kg respectively. Statistically significant asymptotic relationships with parity number were obtained for live weight and various tissue weights. Nominal mature weight was assumed to be 0·99 of the asymptote and nominal mature weights for live weight, carcass weight, saleable beef, separable lean and separable fat were attained at parity (age, years) 4/5 (6·1), 3/4 (5·3), 4/5 (6·2), 3/4 (4·7) and 1/2 (2·7) respectively. The proportion of high-priced joints declined with increase in parity (linear trend P 0·01) thus indicating a reduction in relative growth of the main muscle groups of the hind limb. Shear value measurements on cooked muscle from the maiden and first parity animals indicated very tender meat, but tenderness declined with increase in parity (asymptotic trend P 0·001). Taking animals to the higher level of finish effected increases in carcass weight and separable fat in the carcass of 55·1 kg (P 0·001) and 72 g/kg (P 0·001) respectively, while separable lean declined by 53 g/kg (P 0·001). Cooked muscle from the high finish animals was less tender than from the medium finish animals (P 0·05). It was concluded that slaughtering cows at the third parity would enable a self replacing closed herd to be operated, while obtaining 0·97 of the potential maximum output of saleable beef from the cow, but there may be some compromise in the tenderness of the meat for some consumers by the third parity.

Validation of Welfare Assessment Methods at Herd Level: An Example

Validity is a measure of the extent to which a method measures what it is supposed to measure. It is therefore a relative concept in the sense that it is defined in relation to the aim of the method. The comparison of methods with similar aims can provide information on validity. In order to make such a comparison, welfare was evaluated in 10 Danish dairy herds using two different welfare assessment methods: an environment-based method (TGI200) and an animal-based method (factor analysis of individual animal observations). The results of the assessments were compared, and their agreement and disagreement were investigated and discussed. A fairly non-significant correlation (r=0.31, p=0.38, n=10) was observed between the two methods.

Cattle response to foot‐and‐mouth disease virus nonstructural proteins as antigens within vaccines produced using different concentrations

Four groups of ten nine‐month‐old Nelore heifers were used for this study. Each group received one of four foot‐and‐mouth disease (FMD) trivalent vaccines for the duration of the experiment. The four vaccine formulations (Normal, 2X, 4X and 8X) differed in 140S content to determine the serological reactivities to FMD virus (FMDV) nonstructural proteins 2C, 3ABC and 3D. Vaccination was by the intramuscular administration of vaccine on day 0, 180 and 360. Bleedings were done at 30 days post vaccination (dpv), 90 dpv, 30 days post revaccination (dpr), 90 dpr, and 30 days post third administration (dprr). There was a general tendency to have higher mean 3D responses with increased vaccine application but not with increased concentration of antigen. With 2C and 3ABC this tendency was not seen, neither with repeated application of vaccine nor with increased antigen concentration. All individual animal observations to 2C and 3ABC remained within three standard deviations of the average observed for naive bovids. Percent of positive (PP) reactions was determined using an ELISA for nonstructural proteins 2C, 3ABC and 3D expressed in baculovirus as previously described. A value of >25 PP to 2C or 3ABC could be considered as an indication of previous infection or of the presence of viral activity. PP results between 18 and 25 PP suggest viral activity and animals should be retested. Those responses below 15 PP are suggestive of vaccination or naive status. As diagnosis in the laboratory is not divorced from the field epidemiological scene, the intermediate zone between 10 and 20 PP should be considered and acted upon according to the overall zoosanitary situation of that country or region and the purposes of the ongoing FMD control efforts.

Detergent Fiber Traits to Predict Productive Energy of Forages Fed Free Choice to Nonlactating Dairy Cattle

Hay crops that were predominately alfalfa, clover or grass, and silage corn were harvested at early and late maturities to give a wide range in fiber contents. Hay crops were stored as field-cured hay and wilted silage. Each was fed for ad libitum intake to three or more nonlactating digestibility and calorimetry to measure DM intake, TDN, digestible energy, and metabolizable energy. Various fiber components (ADF, NDF, lignin), and expressions computed from them, were used to estimate TDN and digestible energy of forages or groups of forages having homogenous relationships. Stepwise backward elimination procedure was employed to discard independent variables or their squared terms at 5% probability to develop significant, biologically sound, practical predictive relationships. Standard NRC equations were used to extend energy densities to NEL. Free choice DM intake of forages (fed alone) was not closely related to NDF percentages. Estimates of NEL generally were higher when computed via TDN as opposed to digestible energy or metabolizable energy, which did not differ. We present equations to predict NEL from ADF via digestible energy for legumes, grasses, and corn silage, which differ markedly, in some cases, from those m current use. Equations for grasses had low r2, but these could not be compared with published ones, which apparently are based on treatment means rather than individual-animal observations. The NEL of hay crops and corn silages also were predicted from an expression of lignified NDF in DM with moderate precision.