The nutritive value of 26 agro-industrial by-products was assessed from their chemical composition, in vitro digestibility and rumen fermentation kinetics. By-products from sugar beet, grape, olive tree, almond, broccoli, lettuce, asparagus, green bean, artichoke, peas, broad beans, tomato, pepper, apple pomace and citrus were evaluated. Chemical composition, in vitro digestibility and fermentation kinetics varied largely across the by-products. Data were subjected to multivariate and principal component analyses (PCA). According to a multivariate cluster analysis chart, samples formed four distinctive groups (A–D). Less degradable by-products were olive tree leaves, pepper skins and grape seeds (group A); whereas the more degradable ones were sugar beet, orange, lemon and clementine pulps (group D). In the PCA plot, component 1 segregated samples of groups A and B from those of groups C and D. Considering the large variability among by-products, most of them can be regarded as potential ingredients in ruminant rations. Depending on the characteristic nutritive value of each by-product, these feedstuffs can provide alternative sources of energy (e.g., citrus pulps), protein (e.g., asparagus rinds), soluble fibre (e.g., sugar beet pulp) or less digestible roughage (e.g., grape seeds or pepper skin).

Supplementing a diet with nitrate is regarded as an effective and promising methane (CH₄) mitigation strategy by competing with methanogens for available hydrogen through its reduction of ammonia in the rumen. Studies have shown major reductions in CH₄ emissions with nitrate supplementation, but with large variation in response. The objective of this study was to quantitatively investigate the effect of dietary nitrate on enteric CH₄ production and yield and evaluate the variables with high potential to explain the heterogeneity of between-study variability using meta-analytical models. A data set containing 56 treatments from 24 studies was developed to conduct a meta-analysis. Dry matter (DM) intake, nitrate dose (g/kg of DM), animal body weight, roughage proportion of diet, dietary crude protein and neutral detergent fiber content, CH₄ measurement technique, and type of cattle (beef or dairy) were considered as explanatory variables. Average DM intake and CH₄ production for dairy cows (16.2 ± 2.93 kg/d; 311 ± 58.8 g/d) were much higher than for beef cattle (8.1 ± 1.57 kg/d; 146 ± 50.9 g/d). Therefore, a relative mean difference was calculated and used to conduct random-effect and mixed-effect model analysis to eliminate the large variations between types of animal due to intake. The final mixed-effect model for CH₄ production (g of CH₄/d) had 3 explanatory variables and included nitrate dose, type of cattle, and DM intake. The final mixed-effect model for CH₄ yield (g of CH₄/kg of DM intake) had 2 explanatory variables and included nitrate dose and type of cattle. Nitrate effect sizes on CH₄ production (dairy: -20.4 ± 1.89%; beef: -10.1 ± 1.52%) and yield (dairy: -15.5 ± 1.15%; beef: -8.95 ± 1.764%) were significantly different between the 2 types of cattle. When data from slow-release nitrate sources were removed from the analysis, there was no significant difference in type of cattle anymore for CH₄ production and yield. Nitrate dose enhanced the mitigating effect of nitrate on CH₄ production and yield by 0.911 ± 0.1407% and 0.728 ± 0.2034%, respectively, for every 1 g/kg of DM increase from its mean dietary inclusion (16.7 g/kg of DM). An increase of 1 kg of DM/d in DM intake from its mean dietary intake (11.1 kg of DM/d) decreased the effect of nitrate on CH₄ production by 0.691 ± 0.2944%. Overall, this meta-analysis demonstrated that nitrate supplementation reduces CH₄ production and yield in a dose-dependent manner, and that elevated DM intake decreases the effect of nitrate supplementation on CH₄ production. Furthermore, the stronger antimethanogenic effect on CH₄ production and yield in dairy cows than in beef steers could be related to use of slow-release nitrate in beef cattle.

The objective of the study was to revise a model of P kinetics proposed by Vitti et al. (2000) and extend its use to study Ca flows in growing sheep. Twelve Santa Ines male sheep, 8 mo of age, with average BW of 31.6 kg were injected with ³²P and ⁴⁵Ca to trace the movement of P and Ca in the body. The original model had 4 pools representing the gut, plasma, soft tissues, and bone. In the revised model, instantaneous values rather than averages for pool derivatives were incorporated, and the model was extended to represent absorption and excretion of phytate P explicitly. The amendments improved the model, resulting in higher flows between plasma and bone than between plasma and tissue and, therefore, a more accurate representation of P metabolism. Phosphorus and Ca metabolism were then assessed conjointly using the revised model. The results showed that P and Ca metabolism are closely related as evidenced by the ratio of these minerals in the bidirectional flows between plasma and bone and between plasma and tissue. Phytate P digestibility was 47%, and P retention was negative (−1.4 g/d), suggesting that a feed characteristic impaired P utilization and led to P deficiency. The revised model provides an improved prediction of P and Ca metabolism that can be used to assess mineral requirements and to estimate losses to the environment. Copyright © 2006. . Copyright 2006 Journal of Animal Science

A 305-d lactation followed by a 60-d dry period has traditionally been considered economically optimal, yet dairy cows in modern intensive dairy systems are frequently dried off while still producing significant quantities of milk. Managing cows for an extended lactation has reported production, welfare and economic benefits, but not all cows are suitable for an extended lactation. Implementation of an extended lactation strategy on-farm could benefit from use of a decision support system, based on a mathematical lactation model, that can identify suitable cows during early lactation that have a high likelihood of producing above a target milk yield (MY) at 305 d in milk (DIM). Therefore, our objectives were 1) to compare the suitability of 3 commonly used lactation models for modeling extended lactations (Dijkstra, Wood, and Wilmink) in primiparous and multiparous cows under a variety of lactation lengths, and 2) to determine the amount of early lactation daily MY data needed to accurately forecast MY at d 305 by using the most suitable model and determine if this is sufficient for identifying cows suitable for an extended lactation before the end of a typical voluntary waiting period (50 to 90 d). Daily MY data from 467 individual Holstein-Friesian lactations (DIM >305 d; 379 ± 65 d lactation length [mean ± SD]) were fitted by the 3 lactation models using a nonlinear regression procedure. The parameter estimates of these models, lactation characteristics (peak yield, time to peak yield, and persistency), and goodness-of-fit were compared between parity and different lactation lengths. The models had similar performance and differences between parity groups were consistent with previous literature. Then, data from only the first i DIM for each individual lactation, where i was incremented by 30 d from 30 to 150 DIM and by 50 d from 150 to 300 DIM, were fitted by each model to forecast MY at d 305. The Dijkstra model was selected for further analysis as it had superior goodness-of-fit statistics for i = 30 and 60. The data set was fit twice by the Dijkstra model, with parameter bounds either unconstrained or constrained. The quality of predictions of MY at d 305 improved with increasing data availability for both models and assisting the model fitting procedure with more biologically relevant constraints on parameters improved the predictions, but neither was reliable enough for practical use on-farm due to the high uncertainty of forecasted predictions. Using 90 d of data, the constrained model correctly classified 66% of lactations as being above or below a target MY at d 305 of 25 kg/d, with a probability threshold of 0.95. The proportion of correct classifications became smaller at lower targets of MY at d 305 and became greater when using more lactation days. Overall, further work is required to develop a model that can forecast late lactation MY with sufficient accuracy for practical use. We envisage that a hybridized machine learning and mechanistic model that incorporates additional historical and genetic information with early lactation MY could produce meaningful lactation curve forecasts.

The high contribution of postruminal starch digestion (up to 50%) to total-tract starch digestion on energy-dense, starch-rich diets demands that limitations to small intestinal starch digestion be identified. A mechanistic model of the small intestine was described and evaluated with regard to its ability to simulate observations from abomasal carbohydrate infusions in the dairy cow. The 7 state variables represent starch, oligosaccharide, glucose, and pancreatic amylase in the intestinal lumen, oligosaccharide and glucose in the unstirred water layer at the intestinal wall, and intracellular glucose of the enterocyte. Enzymatic hydrolysis of starch was modeled as a 2-stage process involving the activity of pancreatic amylase in the lumen and of oligosaccharidase at the brush border of the enterocyte confined within the unstirred water layer. The Na+-dependent glucose transport into the enterocyte was represented along with a facilitative glucose transporter 2 transport system on the basolateral membrane. The small intestine is subdivided into 3 main sections, representing the duodenum, jejunum, and ileum for parameterization. Further subsections are defined between which continual digesta flow is represented. The model predicted nonstructural carbohydrate disappearance in the small intestine for cattle unadapted to duodenal infusion with a coefficient of determination of 0.92 and a root mean square prediction error of 25.4%. Simulation of glucose disappearance for mature Holstein heifers adapted to various levels of duodenal glucose infusion yielded a coefficient of determination of 0.81 and a root mean square prediction error of 38.6%. Analysis of model behavior identified limitations to the efficiency of small intestinal starch digestion with high levels of duodenal starch flow. Limitations to individual processes, particularly starch digestion in the proximal section of the intestine, can create asynchrony between starch hydrolysis and glucose uptake capacity.

Current feed evaluation systems for ruminants are too imprecise to describe diets in terms of their acidosis risk. The dynamic mechanistic model described herein arises from the integration of a lactic acid (La) metabolism module into an extant model of whole-rumen function. The model was evaluated using published data from cows and sheep fed a range of diets or infused with various doses of La. The model performed well in simulating peak rumen La concentrations (coefficient of determination = 0.96; root mean square prediction error = 16.96% of observed mean), although frequency of sampling for the published data prevented a comprehensive comparison of prediction of time to peak La accumulation. The model showed a tendency for increased La accumulation following feeding of diets rich in nonstructural carbohydrates, although less-soluble starch sources such as corn tended to limit rumen La concentration. Simulated La absorption from the rumen remained low throughout the feeding cycle. The competition between bacteria and protozoa for rumen La suggests a variable contribution of protozoa to total La utilization. However, the model was unable to simulate the effects of defaunation on rumen La metabolism, indicating a need for a more detailed description of protozoal metabolism. The model could form the basis of a feed evaluation system with regard to rumen La metabolism.