Models to predict nitrogen excretion from beef cattle fed a wide range of diets compiled from South America

The objective of this meta-analysis was to develop and evaluate models for predicting nitrogen (N) excretion in feces, urine, and manure in beef cattle in South America. The study incorporated a total of 1,116 individual observations of N excretion in feces and 939 individual observations of N excretion in feces and in urine (g/d), representing a diverse range of diets, animal genotypes, and management conditions in South America. The dataset also included data on dry matter intake (DMI; kg/d) and nitrogen intake (NI; g/d), concentrations of dietary components, as well as average daily gain (ADG; g/d) and average body weight (BW; kg). Models were derived using linear mixed-effects regression with a random intercept for the study. Fecal N excretion was positively associated with DMI, NI, nonfibrous carbohydrates, average BW, and ADG and negatively associated with EE and CP concentration in the diet. The univariate model predicting fecal N excretion based on DMI (model 1) performed slightly better than the univariate model, which used NI as a predictor variable (model 2) with a root mean square error (RMSE) of 38.0 vs. 39.2%, the RMSE-observations SD ratio (RSR) of 0.81 vs. 0.84, and concordance correlation coefficient (CCC) of 0.53 vs. 0.50, respectively. Models predicting urinary N excretion were less accurate than those derived to predict fecal N excretion, with an average RMSE of 43.7% vs. 37.0%, respectively. Urinary and manure N excretion were positively associated with DMI, NI, CP, average BW, and ADG and negatively associated with neutral detergent fiber concentration in the diet. As opposed to fecal N excretion, the univariate model predicting urinary N excretion using NI (model 10) performed slightly better than the univariate model using DMI (model 9) as predictor variable with an RMSE of 36.0% vs. 39.7%, RSR 0.85 vs. 0.93, and CCC of 0.43 vs. 0.29, respectively. The models developed in this study are applicable for predicting N excretion in beef cattle across a broad spectrum of dietary compositions and animal genotypes in South America. The univariate model using DMI as a predictor is recommended for fecal N prediction, while the univariate model using NI is recommended for predicting urinary and manure N excretion because the use of more complex models resulted in little to no benefits. However, it may be more useful to consider more complex models that incorporate nutrient intakes and diet composition for decision-making when N excretion is a factor to be considered. Three extant equations evaluated in this study have the potential to be used in tropical conditions typical of South America to predict fecal N excretion with good precision and accuracy. However, none of the extant equations are recommended for predicting urine or manure N excretion because of their high RMSE, and low precision and accuracy.

Nitrogen balance in dairy cows fed low-nitrogen diets based on various proportions of fresh grass and maize silage

To ensure sustainable and efficient production, dairy farms must reduce their environmental impacts and nitrogen losses, which are sources of pollution, while increasing their feed self-sufficiency. Grass-based dairy systems, frequently combine fresh grass with maize silage when grass is scarce or during dietary transitions. However, the effects of combining fresh grass and maize silage on cow performance and N excretion are poorly known. This study aimed to quantify the effects of increasing the proportion of maize silage in a fresh grass diet on cow N flows and metabolism, in the context of grass-based dairy systems. Four proportions of maize silage in a fresh grass diet (objectives of 0, 17, 34 and 51% DM of maize silage) were investigated. The experiment was performed in a 4 × 3 Latin square design using eight lactating cows during three 3-week periods. DM intake (DMI), milk yield, faeces and urine outputs, and their N concentrations were measured for each cow. The fresh grass CP concentration was lower than planned (106 ± 13.0 g/kg DM). This resulted in very low dietary CP concentration, which decreased from 108 to 86 g/kg DM when maize silage in the diet increased from 0 to 51% DM, respectively. DM intake and milk yield both decreased linearly by 3.3 kg/day from 0 to 51% DM of maize silage in the diet. Thus, N intake decreased linearly by 100 g/day from 0 to 51% DM of maize silage in the diet. The N concentration of milk was highest for the diet with 0% DM of maize silage. Nitrogen excreted in faeces and urine decreased linearly by 29 and 23 g/day, respectively, from 0 to 51% DM of maize silage in the diet. The low dietary N concentration resulted in low ruminal NH3-N concentrations (8 mg/L, on average) and urinary urea excretion (down to 8% urea N in urinary N). Increasing the proportion of maize silage in an unusually low-N grass diet, without protein-rich concentrates, induced highly N-deficient diets with minimal N losses in faeces and urine but large and unsustainable decreases in DMI and milk yield.

A new protein requirement system for dairy cows

Accurate prediction of protein requirements for maintenance and lactation is needed to develop more profitable diets and reduce N loss and its environmental impact. A new factorial approach for accounting for net protein requirement for maintenance (NP_M) and metabolizable protein (MP) efficiency for lactation (EMP_L) was developed from a meta-analysis of 223 N balance trials. We defined NP_M as the sum of the endogenous protein fecal and urinary excretion and estimated it from the intercept of a nonlinear equation between N intake and combined total N fecal and urinary excretion. Our model had a strong goodness-of-fit to estimate NP_M (6.32 ± 0.15 g protein/kg metabolic body weight; n = 807 treatment means; r = 0.91). We calculated the EMP_L as a proportion of the N intake, minus N excreted in feces and urine, that was secreted in milk. A fixed-EMP_L value of 0.705 ± 0.020 was proposed. In a second independent data set, nonammonia-nonmicrobial-N and microbial-N ruminal outflows were measured, and the adequacy of the MP prediction (51 studies; n = 192 means treatments) was assessed. Our system based on the fixed-EMP_L model predicted the MP requirement for lactation and maintenance with higher accuracy than several North American and European dairy cattle nutrition models, including the INRA (2018) and NASEM (2021). Only the NRC (2001), CNCPS 6.5, and Feed into Milk (2004) models had similar accuracy to predict MP requirement. Our system may contribute to improve the prediction for MP requirements of maintenance and lactation. However, most refined predictive models of intestinal digestibility for rumen undegradable protein and microbial protein are still needed to reduce the evaluation biases in our model and external models for predicting the MP requirements of dairy cows.

Review: Impact of protein and energy supply on the fate of amino acids from absorption to milk protein in dairy cows

Making dairy farming more cost-effective and reducing nitrogen environmental pollution could be reached through a reduced input of dietary protein, provided productivity is not compromised. This could be achieved through balancing dairy rations for essential amino acids (EAA) rather than their aggregate, the metabolizable protein (MP). This review revisits the estimations of the major true protein secretions in dairy cows, milk protein yield (MPY), metabolic fecal protein (MFP), endogenous urinary loss and scurf and associated AA composition. The combined efficiency with which MP (EffMP) or EAA (EffAA) is used to support protein secretions is calculated as the sum of true protein secretions (MPY + MFP + scurf) divided by the net supply (adjusted to remove the endogenous urinary excretion: MPadj and AAadj). Using the proposed protein and AA secretions, EffMP and EffAA were predicted through meta-analyses (807 treatment means) and validated using an independent database (129 treatment means). The effects of MPadj or AAadj, plus digestible energy intake (DEI), days in milk (DIM) and parity (primiparous v. multiparous), were significant in all models. Models using (MPadj, MPadj × MPadj, DEI and DEI × DEI) or (MPadj/DEI and MPadj/DEI × MPadj/DEI) had similar corrected Akaike's information criterion, but the model using MPadj/DEI performed better in the validation database. A model that also included this ratio was, therefore, used to fitting equations to predict EffAA. These equations predicted well EffAA in the validation database except for Arg which had a strong slope bias. Predictions of MPY from predicted EffMP based on MPadj/DEI, MPadj/DEI × MPadj/DEI, DIM and parity yielded a better fit than direct predictions of MPY based on MPadj, MPadj × MPadj, DEI, DIM and parity. Predictions of MPY based on each EffAA yielded fairly similar results among AA. It is proposed to ponder the mean of MPY predictions obtained from each EffAA by the lowest prediction to retain the potential limitation from AA with the shortest supply. Overall, the revisited estimations of endogenous urinary excretion and MFP, revised AA composition of protein secretions and inclusion of a variable combined EffAA (based on AAadj/DEI, AAadj/DEI × Aadj/DEI, DIM and parity) offer the potential to improve predictions of MPY, identify which AA are potentially in short supply and, therefore, improve the AA balance of dairy rations.

ASN-ASAS SYMPOSIUM: FUTURE OF DATA ANALYTICS IN NUTRITION: Mathematical modeling in ruminant nutrition: approaches and paradigms, extant models, and thoughts for upcoming predictive analytics1,2

This paper outlines typical terminology for modeling and highlights key historical and forthcoming aspects of mathematical modeling. Mathematical models (MM) are mental conceptualizations, enclosed in a virtual domain, whose purpose is to translate real-life situations into mathematical formulations to describe existing patterns or forecast future behaviors in real-life situations. The appropriateness of the virtual representation of real-life situations through MM depends on the modeler's ability to synthesize essential concepts and associate their interrelationships with measured data. The development of MM paralleled the evolution of digital computing. The scientific community has only slightly accepted and used MM, in part because scientists are trained in experimental research and not systems thinking. The scientific advancements in ruminant production have been tangible but incipient because we are still learning how to connect experimental research data and concepts through MM, a process that is still obscure to many scientists. Our inability to ask the right questions and to define the boundaries of our problem when developing models might have limited the breadth and depth of MM in agriculture. Artificial intelligence (AI) has been developed in tandem with the need to analyze big data using high-performance computing. However, the emergence of AI, a computational technology that is data-intensive and requires less systems thinking of how things are interrelated, may further reduce the interest in mechanistic, conceptual MM. Artificial intelligence might provide, however, a paradigm shift in MM, including nutrition modeling, by creating novel opportunities to understand the underlying mechanisms when integrating large amounts of quantifiable data. Associating AI with mechanistic models may eventually lead to the development of hybrid mechanistic machine-learning modeling. Modelers must learn how to integrate powerful data-driven tools and knowledge-driven approaches into functional models that are sustainable and resilient. The successful future of MM might rely on the development of redesigned models that can integrate existing technological advancements in data analytics to take advantage of accumulated scientific knowledge. However, the next evolution may require the creation of novel technologies for data gathering and analyses and the rethinking of innovative MM concepts rather than spending resources in collecting futile data or amending old technologies.

Predicting nitrogen excretion from cattle

Manure nitrogen (N) from cattle production facilities can lead to negative environmental effects, such as contribution to greenhouse gas emissions, leaching and runoff to aqueous ecosystems leading to eutrophication, and acid rain. To mitigate these effects and to improve the efficiency of N use, accurate prediction of N excretion and secretions are required. A genetic algorithm was implemented to select models to predict fecal, urinary, and total manure N excretions, and milk N secretions from 3 classes of animals: lactating dairy cows, heifers and dry cows, and steers. Two tiers of model classes were developed for each category of animals based on model input requirements. A total of 6 models for heifers and dry cows and steers and an additional 2 models for lactating dairy cattle were developed. Evaluation of the models using K-fold cross validation based on all data and using the most recent 6 yr of data showed better prediction for total manure N and fecal N compared with urinary N excretion, which was the most variable response in the database. Compared with extant models from the literature, the models developed in this study resulted in a significant improvement in prediction error for fecal and urinary N excretions from lactating cows. For total manure production by lactating cows, extant and new models were comparable in their prediction ability. Both proposed and extant models performed better than the prediction methods used by the US Environmental Protection Agency for the national inventory of greenhouse gases. Therefore, the proposed models are recommended for use in estimation of manure N from various classes of animals.

Nitrogen digestion and urea recycling in Hokkaido native horses fed hay-based diets

Nitrogen (N) digestion and urea-N metabolism in Hokkaido native horses fed roughage-based diets containing different types and levels of protein sources were studied. Horses (173 ± 4.8 kg) fitted with an ileum cannula were fed four diets consisting of 100% timothy hay (TH), 88% TH and 12% soybean meal (SBM), 79% TH and 21% SBM, and 51% TH and 49% alfalfa hay at 2.2% of body weight. Dietary protein content varied from 5% to 15% of dry matter. Apparent N digestibilities in the pre-cecum and total tract for the TH diet were lower than those for other diets. However, the proportion of post-ileum N digestion to N intake was not affected by the diets. Urea-N production was linearly related to N intake, but gut urea-N entry was not affected by the diets. The proportion of gut urea-N entry to urea-N production tended to be higher for the TH diet (57%) than the two SBM diets (39%). Anabolic use of urea-N entering the gut was not affected by the diets (20-36% of gut urea-N entry). These results indicate that urea-N recycling provides additional N sources for microbial fermentation in the hindgut of Hokkaido native horses fed low-quality roughages.

Effect of nutrient composition of feeds on digestibility of organic matter by cattle: a review

Estimates of nutrient availability, calculated as TDN for 106 different feedstuffs generated from various published equations, were compared with TDN for similar feeds listed in the 1961 text by F. B. Morrison titled Feeds and Feeding. Incomplete analysis of carbohydrate fractions limited accuracy of evaluations. Although published equations may satisfactorily rank feeds in energy value, the absolute values, correlations, and SE of the estimates revealed that most equations were inaccurate. Across all feeds and forages, TDN was related most closely to crude fiber (R(2) = 0.68) within data sets from Morrison's text and from the NRC publications concerning Nutrient Requirements for Dairy and for Beef Cattle in 1989 and 2000, respectively. Within the latter data set, of the total variation, ADF accounted for 65% of the variation in TDN across all feeds, 62% of the variation in TDN for concentrate feeds, but only 41% of the variation in TDN of forages. Within the 2001 publication for dairy cattle from the NRC, ADF content was related most closely to TDN for all feeds, but nonfiber carbohydrate was most closely related to TDN of forages (R(2) = 0.81 and 0.69, respectively). To separate true from apparent digestibility of nutrients, fecal excretions of components (i.e., CP, fat, crude fiber, nitrogen free extract) were regressed against concentrations of these nutrients in feedstuffs and summed to estimate fecal loss. Metabolic fecal loss of OM (MFOM), the difference between true and apparent OM digestibility, was correlated closely with crude fiber content of feedstuffs (R(2) = 0.86) and increased from 7 to 25 g/100 g of diet as dietary crude fiber concentration increased. This may explain why most TDN equations are based on crude fiber or ADF. Whether dietary NDF similarly increases metabolic OM excretion is not certain, but when humans were fed NDF-enriched diets, fecal excretion of nonfiber carbohydrate increased markedly. The impact of crude fiber on nutrient availability of feeds appears to be related more closely to its adverse effect on apparent digestibility of other nutrients than to the amount of energy that fiber itself contributes. Refinements to laboratory methods for measuring fiber digestibility that match apparent in vivo digestibility coefficients for fiber by ruminants is needed, and the origin, composition, and cost of replacing MFOM need additional research.

Supplementation of methionine and selection of highly digestible rumen undegradable protein to improve nitrogen efficiency for milk production

Metabolizable protein (MP) supply and amino acid balance were manipulated through selection of highly digestible rumen-undegradable protein (RUP) sources and methionine (Met) supplementation. Effects on production efficiency and N utilization of lactating dairy cows were determined. Thirty-two multiparous (647 kg) and 28 primiparous (550 kg) Holstein cows were assigned during the fourth week of lactation to one of four dietary treatments. Treatments were 1) 18.3% crude protein (CP) with low estimated intestinal digestibility of RUP (HiCP-LoDRUP), 2) 18.3% CP with high digestibility RUP (HiCP-HiDRUP), 3) 16.9% CP with high digestibility RUP (LoCP-HiDRUP), and 4) 17.0% CP with high digestibility RUP and supplemental Met (LoCP-HiDRUP + Met). Diets were balanced to have equal concentrations of net energy for lactation (NE(L)), acid detergent fiber (ADF), neutral detergent fiber (NDF), and ash. Milk yields (40.8, 46.2, 42.9, 46.6 kg/d), protein percentages (2.95, 2.98, 2.99, 3.09%), and fat percentages (3.42, 3.64, 3.66, 3.73%) are reported here for HiCP-LoDRUP, HiCP-HiDRUP, LoCP-HiDRUP, and LoCP-HiDRUP + Met, respectively. Milk urea N and BUN decreased when feeding a lower CP diet. Efficiency of use of N for milk protein production was higher when feeding higher digestibility RUP, especially with the LoCP-HiDRUP + Met diet. A digestibility study followed the production trial, with six cows per treatment group continuing on the same treatment for an additional week. The experimental periods were 5 d long, with 1 d of adjustment and 4 d of total collection of urine and feces. Dry matter intake, milk production, milk protein production, and N digestibility were not significantly different among treatments during the collection trial, whereas N intake and N absorbed increased with the higher CP diets. The quantity of N in feces did not change with diet, but quantity of N in urine decreased in the low CP diets. Milk N as a percentage of intake N and milk N as a percentage of N absorbed showed a trend toward increasing as CP concentration in the diet decreased. The supplementation of Met did not improve the efficiency of N utilization during the digestibility study, in contrast to what was estimated during the production trial. Supplementing the highly digestible RUP source with rumen available and rumen escape sources of Met resulted in maximal milk and protein production and maximum N efficiency by cows during the production trial, indicating that postruminal digestibility of RUP and amino acid balance can be more important than total RUP supplementation.

Evaluation of starter diet crude protein level and feeding regimen for calves weaned at 26 days of age

The objectives of this study were to evaluate two levels of CP in starter diets offered to calves weaned on d 26 and to compare postweaning intake of starter diets and performance of calves weaned at 26 or 42 d. Forty Holstein calves were housed in individual outdoor hutches and assigned to one of four treatments. Treatments consisted of weaning on d 26 after offering a 20% CP starter diet from d 1, or d 21, or after offering a 25% CP starter diet from d 21, or weaning on d 42 after offering a 20% CP starter diet from d 1 (control). Between d 42 and 84, calves were commingled in outdoor group pens. On d 42, BW of control calves was heavier than that of early weaned calves. On d 84, BW did not differ among treatments. Consumption of starter diet was not affected by the age at which it was first offered. Weaned calves consumed more starter diet than controls from d 29 to 42. Postweaning performance was not affected by increasing CP in the starter diet. Weaning treatment did not affect the incidence of scours. Comparison of CP intakes with current NRC recommendations suggests that CP requirements for young milk-fed calves are adequate.

Digestibility of nitrogen in heat-damaged alfalfa

In order to partition indigestible N in heat-damaged forages into its various chemical fractions, alfalfa (41.1% dry matter), stored in an upright stave silo, was not sealed, and heating was promoted. Silage was labeled as unheated silage (temperature below 20 degrees C); 35 degrees C silage (temperature near 35 degrees C, 5220 degrees C d); and 60 degrees C silage (temperature near 60 degrees C, 10,800 degrees C d). Total amino acid content of 60 degrees C silage was lower and acid detergent insoluble amino acid content was higher than in 35 degrees C or unheated silages. Apparent N digestibilities, determined using lactating dairy cows, were 47.5, 48.4, and 63.6% for 60 degrees C, 35 degrees C, and unheated silages. Estimates of true N digestibility were 76, 76, and 90%; true digestibility coefficients for amino acid N were 86, 92, and 95%; and true digestion coefficients for nonamino acid N were 67, 45, and 69% for 60 degrees C, 35 degrees C, and unheated silages. Two possible causes for depressed N digestibility of 60 degrees C silage were 1) the ratio of poorly digested nonamino acid N to highly digested amino acid N was higher in 60 degrees C silage than in 35 degrees C or unheated silage, and 2) amino acid N in 60 degrees C silage was digested less than amino acid N in 35 degrees C or unheated silage. Reasons for low digestibility of N in 35 degrees C silage are less clear, but evidence suggests formation of indigestible N complexes in digesta.

Effect of protein on growth of Holstein heifer calves from 2 to 10 months

Two trials involving 44 weaned Holstein heifer calves were to determine effects of protein on growth in weight and wither height, dry matter intake, and digestibility. In trial 1, 24 calves were fed according to requirements from 85 to 182 d of age, then at 80, 100, or 120% of recommended amounts for dietary protein content until 295 d. Weight at 295 d increased linearly with protein (280, 287, 298 kg), but wither height and dry matter intake were unaffected. In trial 2, 20 calves were fed 80, 100, or 120% of recommended protein from 71 to 182 d. Weight and wither height at 182 d responded quadratically to protein with highest values from the medium protein diet. From 183 to 295 d, trial 2 calves were rerandomized to diets of medium or high protein. Weight at 295 d was increased by high protein. An interaction between treatments for wither height indicated responses to high protein after 182 d for calves on low or medium protein before that time and a decrease for calves on high protein throughout. In male calves of similar age, elevated protein increased apparent protein digestibility but had inconsistent effects on digestibility of dry matter. Current protein recommendations appear adequate for calves to 6 mo of age but thereafter may be too generous.