ALBERT

Description: Perception of freshwater use varies between nations and has led to concerns of how to evaluate water use for sustainable food production. The water footprint of beef cattle (WFB) is an important metric to determine current levels of freshwater use and to set sustainability goals. However, current WFB publications provide broad WF values with inconsistent units preventing direct comparison of WFB models. The water footprint assessment (WFA) methodologies use static physio-enviro-managerial equations, rather than dynamic, which limits their ability to estimate cattle water use. This study aimed to advance current WFA methods for WFB estimation by formulating the WFA into a system dynamics methodology to adequately characterize the major phases of the beef cattle industry and provide a tool to identify high-leverage solutions for complex water use systems. Texas is one of the largest cattle producing areas in the United States, a significant water user. This geolocation is an ideal template for WFB estimation in other regions due to its diverse geography, management-cultures, climate and natural resources. The Texas Beef Water Footprint model comprised seven submodels (cattle population, growth, nutrition, forage, WFB, supply chain and regional water use; 1432 state variables). Calibration of our model replicated initial WFB values from an independent study by Chapagain and Hoekstra in 2003 (CH2003). This CH2003 v. Texas production scenarios evaluated model parameters and assumptions and estimated a 41–66% WFB variability. The current model provides an insightful tool to improve complex, unsustainable and inefficient water use systems.

Description: SUMMARYThe objectives of the current study were to determine the variation structure within a day and across days when determining dry matter intake (DMI) of Coastal bermudagrass (Cynodon dactylon (L.) Pers.) pasture using dotriacontane (C32) as an external marker, to determine the optimal times for faecal collection for forage DMI estimation and to compare C31 and C33 as internal markers in estimating forage DMI in Brahman bulls. Sixteen Brahman bulls were allocated by weight to four pastures, and stocked at a moderate to low grazing pressure for 63 days from late June to the end of August. Three intake measurement periods (P1, P2, P3) were used; each period consisting of 10 days of twice daily C32 (400 mg/day) administration. Faecal collections were taken during the last 5 days (07.00, 11.00, 15.00 and 19.00 h). The C32 was individually hand fed using Calan gates, with maize gluten as a carrier for the alkane. Gas chromatography was used to determine n-alkanes in the forage and faecal samples. The concentration of C31 was less than C33 in the bermudagrass for all periods (P  0·05). The average concentration of C32 in the forage was 5·1, 7·6 and 9·6 mg/kg dry matter (DM), for P1, P2 and P3, respectively, with an average of 7·5 mg/kg DM for all periods. During P1 and P2, the estimation of forage DMI using C33 had a better fit (smaller –2 × log and Akaike's information criterion (AIC)) than using C31 either with or without adjustments for C32. The variation in estimated forage DMI decreased when forage C32 was not included. The variances of forage DMI were similar using C31 across days, but the Pearson correlations between days were low, which suggested that several days of collection were needed to estimate forage DMI accurately. Correlations between collection times within days were medium to high for all periods and varied from 0·65 to 0.97 for C31 and from 0·26 to 0·96 for C33. When all periods were analysed together, estimates of forage DMI either using C31 or C33 had low correlations between days of collection. Adjustment for C32 did not improve the variance and (co)variance matrix. In summary, C33/C32 had the lowest variation in estimating forage DMI, and at least 5 days of faecal collection were needed to decrease the variability of estimating forage DMI. The optimum times for faecal collection were 07.00 and 19.00 h, and it was important to adjust for C32 alkane concentration in estimating forage DMI in Brahman bulls grazing Coastal bermudagrass.

Description: SUMMARYIn ruminants, urea nitrogen (N) produced by the liver and recycled to the gastrointestinal tract (GIT) provides a source of N for microbial growth and also conserves N. In this respect, it buffers the dietary supply of N available for microbial growth and microbial protein supply. The equation for recycled N in the National Research Council's (NRC 1996, 2000) beef model is based on relationships between ruminal ammonia and plasma urea N concentrations. The objective of the current paper was to estimate recycled N available for anabolism (i.e., urea N used for anabolism, UUA) using available kinetic data. A meta-analysis was conducted using results reported in nine publications that measured urea N kinetics using the dual-labelled urea technique in growing cattle. Diets used in these experiments were predominantly forage-based. Urea production was linearly related to N intake (NI, g/day). Growing cattle converted 74·5% of the incremental NI to urea N. As NI increased, a smaller proportion of the urea produced was recycled to the GIT. On average, 54·4% of the urea N recycled to the GIT was used for anabolism; however, this percentage was not constant. As NI or dietary crude protein (CP) increased (g/kg dry matter, DM), proportionately less of the urea produced was used for anabolism. Nonlinear equations were developed to predict UUA based on NI or dietary CP in the current database and simulated at 5 or 10 kg of daily DM intake (DMI) over the same range of NI (g/day) and therefore, for diets differing in CP content (g/kg DM). The equation based on NI had a quadratic behaviour and the same estimated UUA for both levels of DMI. The equation based on CP showed a relatively small increase in UUA at low DMI and increased UUA at the higher DMI as NI increased. For both equations and both DMI, the pattern suggested a limit to use of recycled N for anabolism.

Description: SUMMARYThe objectives of the present paper were to develop and evaluate empirical equations to predict fractional passage rate (kp) of forages commonly fed to goats using chemical composition of the diet and animal information. Two databases were created. The first (development database) was assembled from four studies that had individual information on animals, diets and faecal marker concentrations over time (up to 120 h post-feeding); it contained 54 data points obtained from Latin square designs. The second (evaluation database) was built using published information gathered from the literature. The evaluation database was comprised of five studies, containing 39 data points on diverse types of diets and animal breeds. The kp was estimated using a time-dependent model based on the Gamma distribution with at least two and up to 12 (rumen)+one (post-rumen) compartments (i.e. G2G1–G12G1) developed from the development database. Statistical analyses were carried out using standard regression analysis and random coefficient model analysis to account for random sources (i.e. study). The evaluation of the developed empirical equation was conducted using regression analysis adjusted for study effects, concordance correlation coefficient and mean square error of prediction. Sensitivity analyses with the developed empirical equation and comparable published equations were performed using Monte Carlo simulations. The G2G1 model consistently had lower sum of squares of errors and greater relative likelihood probabilities than other GnG1 versions. The kp was influenced by several dietary nutrients, including dietary concentration or intake of components such as lignin, neutral detergent fibre (NDF), hemicellulose, crude protein (CP), acid detergent fibre (ADF) and animal body weight (BW). The selected empirical equation, adjusted for study effects, ($kp_{/{ m h}} = 0{cdot}00161 imes { m NDF}_{{ m g/kg,BW}}^{1{cdot}503 pm 0{cdot}371} imes { m e}^{(0{cdot}022 pm 0{cdot}0097 imes { m BW}_{{ m kg}} - 0{cdot}00375 pm 0{cdot}0013 imes { m NDF}_{{ m g/kg DM}} )} $) had an R2 of 0·623 and root of mean square error (RMSE) of 0·0122/h. The evaluation of the adequacy of the selected equation with the evaluation database indicated no systematic bias (slope not different from 1), but a low accuracy (0·33) and a persistent mean bias of 0·0129/h. The sensitivity analysis indicated that the selected empirical equation was most sensitive to changes in dry matter intake (DMI, kg/d), BW(kg) and NDF (g/kg dry matter) with standardized regression coefficients of 0·98, −0·43 and −0·32, respectively. The sensitivity analysis also indicated that the greatest forage kp in goats is likely to be c. 0·0569/h. The comparison with a previously published empirical equation containing data on cattle, sheep and goats, suggested that the distribution of the present empirical equation, adjusted for mean bias, is wider and that kp of goats might be similar to cattle and sheep when fed high amounts of forage under confinement conditions.

Description: SUMMARYThe objective of the current study was to assess the use of nonlinear mixed model methodology to fit the growth curves (weightv.time) of two dairy goat genotypes (Alpine, +A and Saanen, +S). The nonlinear functions evaluated included Brody, Von Bertalanffy, Richards, Logistic and Gompertz. The growth curve adjustment was performed using two steps. First, random effectsu1,u2andu3were linked to the asymptotic body weight (β1), constant of integration (β2) and rate constant of growth (β3) parameters, respectively. In addition to a traditional fixed-effects model, four combinations of models were evaluated using random variables: all parameters associated with random effects (u1,u2andu3), onlyβ1andβ2(u1andu2), onlyβ1andβ3(u1andu3) and onlyβ1(u1). Second, the fit of the best adjusted model was refined by using the power variance and modelling the error structure. Residual variance ($sigma _e^2 $) and the Akaike information criterion were used to evaluate the models. After the best fitting model was chosen, the genotype curve parameters were compared. The residual variance was reduced in all scenarios for which random effects were considered. The Richards (u1andu3) function had the best fit to the data. This model was reparameterized using two isotropic error structures for unequally spaced data, and the structure known in the literature as SP(MATERN) proved to be a better fit. The growth curve parameters differed between the two genotypes, with the exception of the constant that determines the proportion of the final size at which the inflection point occurs (β4). The nonlinear mixed model methodology is an efficient tool for evaluating growth curve features, and it is advisable to assign biologically significant parameters with random effects. Moreover, evaluating error structure modelling is recommended to account for possible correlated errors that may be present even when using random effects. Different Richard growth curve parameters should be used for the predominantly Alpine and Saanen genotypes because there are differences in their growth patterns.

Description: SUMMARYThe Cornell-Penn-Miner (CPM) Dairy is an applied mathematical nutrition model that computes dairy cattle requirements and the supply of energy and nutrients based on characteristics of the animal, the environment and the physicochemical composition of the feeds under diverse production scenarios. The CPM Dairy was designed as a steady-state model to use rates of degradation of feed carbohydrate and protein and the rate of passage to estimate the extent of ruminal fermentation, microbial growth, and intestinal digestibility of carbohydrate and protein fractions in computing energy and protein post-rumen absorption, and the supply of metabolizable energy and protein to the animal. The CPM Dairy version 3.0 (CPM Dairy 3.0) includes an expanded carbohydrate fractionation scheme to facilitate the characterization of individual feeds and a sub-model to predict ruminal metabolism and intestinal absorption of long chain fatty acids. The CPM Dairy includes a non-linear optimization algorithm that allows for least-cost formulation of diets while meeting animal performance, feed availability and environmental restrictions of modern dairy cattle production. When the CPM Dairy 3.0 was evaluated with data of 228 individual lactating dairy cows containing appropriate information including observed dry matter intake, the linear regression between observed and model-predicted milk production values indicated the model was able to account for 79·8% of the variation. The concordance correlation coefficient (CCC) was high (rc=0·89) without a significant mean bias (0·52 kg/d;P=0·12). The accuracy estimated by the CCC was 0·997. The root of mean square error of prediction (MSEP) was 5·14 kg/d (0·16 of the observed mean) and 87·3% of the MSEP was due to random errors, suggesting little systematic bias in predicting milk production of high-producing dairy cattle. Based upon these evaluations, it was concluded the CPM Dairy 3.0 model adequately predicts milk production at the farm level when appropriate animal characterization, feed composition and feed intake are provided; however, further improvements are needed to account for individual animal variation.