Estimates Of Dry Matter Intake Research Articles

Improving Dry Matter Intake Estimates Using Precision Body Weight on Cattle Grazed on Extensive Rangelands.

An essential component required for calculating stocking rates for livestock grazing extensive rangeland is dry matter intake (DMI). Animal unit months are used to simplify this calculation for rangeland systems to determine the rate of forage consumption and the cattle grazing duration. However, there is an opportunity to leverage precision technology deployed on rangeland systems to account for the individual animal variation of DMI and subsequent impacts on herd-level decisions regarding stocking rate. Therefore, the objectives of this study were, first, to build a precision system model (PSM) to predict total DMI (kg) and required pasture area (ha) using precision body weight (BW), and second, to evaluate differences in PSM-predicted stocking rates compared to the traditional herd-level method using initial or estimated mid-season BW. A deterministic model was constructed in both Vensim (version 10.1.2) and Program R (version 4.2.3) to incorporate individual precision BW data into a commonly used rangeland equation using %BW to estimate individual DMI, daily herd DMI, and area (ha) required to meet animal DMI requirements throughout specific grazing periods. Using the PSM, differences in outputs were evaluated using three scenarios: (1) initial BW (business as usual); (2) average mid-season BW; and (3) individual precision BW using data from two precision rangeland experiments conducted at the South Dakota State University Cottonwood Field Station. The data from the two experiments were used to develop PSM case studies. The trial data were collected using precision weight data (SmartScale™) collected from replacement heifers (Case study 1, n = 60) and steers (Case study 2, n = 254) grazing native rangeland. In Case study 1 (heifers), Scenario 1 versus Scenario 3 resulted in an additional 73.41 ha required. Results from Case study 2 indicated an average additional 4.4 ha required per pasture when comparing Scenario 3 versus Scenario 1. Sensitivity analyses resulted in a difference between maximum and minimum simulated values of 27,995 and 4265 kg forage consumed, and 122 and 8.9 pasture ha required for Case studies 1 and 2, respectively. Thus, results from the scenarios indicate an opportunity to identify both under- and over-stocking situations using precision DMI estimates, which helps to identify high-leverage precision tools that have practical applications for enhancing animal and plant productivity and environmental sustainability on extensive rangelands.

Genomic analysis of feed efficiency traits in beef cattle using random regression models.

Feed efficiency plays a major role in the overall profitability and sustainability of the beef cattle industry, as it is directly related to the reduction of the animal demand for input and methane emissions. Traditionally, the average daily feed intake and weight gain are used to calculate feed efficiency traits. However, feed efficiency traits can be analysed longitudinally using random regression models (RRMs), which allow fitting random genetic and environmental effects over time by considering the covariance pattern between the daily records. Therefore, the objectives of this study were to: (1) propose genomic evaluations for dry matter intake (DMI), body weight gain (BWG), residual feed intake (RFI) and residual weight gain (RWG) data collected during an 84-day feedlot test period via RRMs; (2) compare the goodness-of-fit of RRM using Legendre polynomials (LP) and B-spline functions; (3) evaluate the genetic parameters behaviour for feed efficiency traits and their implication for new selection strategies. The datasets were provided by the EMBRAPA-GENEPLUS beef cattle breeding program and included 2920 records for DMI, 2696 records for BWG and 4675 genotyped animals. Genetic parameters and genomic breeding values (GEBVs) were estimated by RRMs under ssGBLUP for Nellore cattle using orthogonal LPs and B-spline. Models were compared based on the deviance information criterion (DIC). The ranking of the average GEBV of each test week and the overall GEBV average were compared by the percentage of individuals in common and the Spearman correlation coefficient (top 1%, 5%, 10% and 100%). The highest goodness-of-fit was obtained with linear B-Spline function considering heterogeneous residual variance. The heritability estimates across the test period for DMI, BWG, RFI and RWG ranged from 0.06 to 0.21, 0.11 to 0.30, 0.03 to 0.26 and 0.07 to 0.27, respectively. DMI and RFI presented within-trait genetic correlations ranging from low to high magnitude across different performance test-day. In contrast, BWG and RWG presented negative genetic correlations between the first 3 weeks and the other days of performance tests. DMI and RFI presented a high-ranking similarity between the GEBV average of week eight and the overall GEBV average, with Spearman correlations and percentages of individuals selected in common ranging from 0.95 to 1.00 and 93 to 100, respectively. Week 11 presented the highest Spearman correlations (ranging from 0.94 to 0.98) and percentages of individuals selected in common (ranging from 85 to 94) of BWG and RWG with the average GEBV of the entire period of the test. In conclusion, the RRM using linear B-splines is a feasible alternative for the genomic evaluation of feed efficiency. Heritability estimates of DMI, RFI, BWG and RWG indicate enough additive genetic variance to achieve a moderate response to selection. A new selection strategy can be adopted by reducing the performance test to 56 days for DMI and RFI selection and 77 days for BWG and RWG selection.

328 Comparison of Predicted Nutritional Requirements of Two Feeding Systems for Dairy Cattle Implemented in a Monte-Carlo Simulation Model of Herd Dynamics

Abstract There is increased interest in precision-feeding systems that account for individual-animal variation and temporal dairy herd dynamics to increase feed-efficiency and reduce environmental footprints. The Ruminant Farm Systems (RuFaS; Hansen et al., 2021) model simulates individual animals on a daily timestep using a stochastic, Monte Carlo framework to represent the phenotypic diversity and population dynamics expected in a dairy herd. Within RuFaS, individual animal nutrient requirements are used to inform pen-level diet formulations and feed delivery. However, nutritional requirements for feeding dairy cattle were recently updated via consideration of both animal and diet-related factors. Our aim was to calculate individual animal predictions for dry matter intake (DMI), as well as energy and metabolizable protein requirements, and compare the methods described in the 7th (NRC, 2001) and 8th (NASEM, 2021) editions of the Nutrient Requirements for Dairy Cattle. For that purpose, we simulated dairy herds (ca. 6,000 individual Holstein animals per simulation, mean adult body weight = 671 kg, standard deviation = 79, mean daily milk production = 39.8 kg·day-1·cow-1 , standard deviation = 7.5) for 10+ year time periods with an updated version of RuFaS. The simulated herds provided a wide range of values with which to test the nutrient requirement models, and their implications. The NASEM DMI estimates were less across the board but began to converge towards NRC calculated values at the larger simulated animal body weights. The principal differences in energy requirements between the two models (NRC vs. NASEM) were: 1) predicted requirements for pregnancy were significantly greater for NASEM than NRC across all life history stages; 2) NASEM displayed slightly greater energy requirements for growth in first and second lactation cows, and; 3) NASEM predicted decreased maintenance requirements for all heifer stages but the opposite was true for mature cows. Metabolizable protein requirements were greater following NASEM calculations in heifers over 300 kg, but strikingly similar to NRC-computed values for lactating cows. Overall, results from this modeling study align with our expectations and demonstrate successful implementation of the two nutrition models within the RuFaS model. These outputs confirm the usefulness of data obtained to explore applications of the two nutrient requirement models under different precision feeding management practices and, to some extent, to quantitatively evaluate their viability of the models in edge cases that can occur within the expected variation of a herd’s population. Future work will explore downstream consequences of the nutrient requirement models within the automated, least-cost ration formulation framework using nonlinear programming and metrics related to manure output and storage. These data will guide further developments of RuFaS as whole farm simulation model and provide insights into expected variation in nutritional requirements within a dynamic set of animals.

PSX-15 Precision Beef Dry Matter Intake Estimation on Extensive Rangelands

Abstract Predicting dry matter intake (DMI) for beef cattle on extensive rangelands presents a significant challenge to determining stocking rates. Traditionally, DMI is estimated by taking full body weight (BW) multiplied by a percentage selected based on animal class, production phase, and forage quality, which introduces tremendous levels of accumulated error at the herd level. Animal Unit Months (AUMs) are utilized to simplify the determination of stocking rate (animal units per area per a specific period of time) of pastures. This challenge represents a tremendous opportunity to leverage precision technology to account for individual animal variation in BW and growth, with subsequent impacts on herd-level decisions. Therefore, the objective of this study was to utilize precision livestock technology (PLT) collected data to build a precision system model (PSM) to evaluate the differences in predicted DMI using either initial BW, expected mid-season BW, or PLT measured BW. The PSM model was built utilizing BW data measured using SmartScale (C-Lock Inc.) for 60 days during the summers of 2021 and 2022 on Angus yearling steers (average initial BW 393.71 ± 39.01 and 315.23 ± 53.91 kg, n = 130 and 124, for year 2021 and 2022, respectively) on native pastures at the South Dakota State University Cottonwood Field Station. The PSM evaluated total forage consumption and deterministically estimated hectares of pasture required to meet the herd forage demands relative to available biomass (kg/ha). the PSM estimated 4.49% and 6.94% more DMI at the herd level compared with using initial BW for years 2021 and 22, respectively, and 1.64% less DMI than mid-season BW in 2021. This resulted in an additional 14.03 and 17.95 ha required for years 2021 and 22, respectively, according to PSM estimates compared with initial BW, while animals were understocked by 5 and 0.3 ha for 2021 and 2022, respectively, using mid-season BW. Individuals expressed a divergent growth rate, resulting in greater misalignment between static and PSM predicted DMI as the grazing period progressed (Menendez et al., 2023), indicating greater opportunities for targeted grazing management in long grazing periods. Therefore, applying precision data provides more precise DMI estimates and demonstrates the advantages and disadvantages of herd-level estimates. The use of PSM helps to identify high-leverage precision tools to minimize a performance gap like overgrazing extensive rangeland systems and demonstrates the critical need to develop robust data-collection and processing steps to leverage continuously collected PLT data.

Genomic evaluation of feed efficiency in US Holstein heifers

There is growing interest in improving feed efficiency (FE) traits in dairy cattle. The objectives of this study were to estimate the genetic parameters of RFI and its component traits (Dry matter intake (DMI), metabolic body weight (MBW) and average daily gain (ADG)) of Holstein heifers, and to develop a system for genomic evaluation for RFI in Holstein dairy calves. The RFI data were collected from 6,563 growing Holstein heifers (initial BW = 261 ± 52 kg; initial age = 266 ± 42 d) for 70 d, across 182 trials conducted between 2014 and 2022 at the STgenetics Ohio Heifer Center (South Charleston, Ohio) as part of EcoFeed® program which aims to improve FE by genetic selection. RFI was estimated as the difference between a heifer's actual feed intake and its expected feed intake, which was determined by regression of DMI against mid-point MBW, age, and ADG across each trial. A total of 61,283 single nucleotide polymorphisms were used in genomic analyses. Animals with phenotypes and genotypes were used as training population and 4 groups of prediction population, each with 2,000 animals, were selected from a pool of Holstein animals with genotypes, based on their relationship with the training population. All traits were analyzed using univariate animal model in DMU version 6 software. Pedigree information and genomic information were used to specify genetic relationships to estimate the variance components and genomic estimated breeding values (GEBVs), respectively. Breeding values of prediction population were estimated by using the 2-step approach, i.e., deriving the prediction equation of GEBVs from training population for the estimation of GEBVs of prediction population with only genotypes. Reliability of breeding values were obtained by approximation based on partitioning a function of accuracy of training population GEBVs and magnitudes of genomic relationships between individuals in the training and prediction population. Heifers had DMI (mean ± SD) of 8.11 ± 1.59 kg over the period of trial with growth rate of 1.08 ± 0.25 kg/d. The heritability estimates (mean ± SE) of RFI, MBW, DMI, and growth rate were 0.24 ± 0.02, 0.23 ± 0.02, 0.27 ± 0.02, and 0.19 ± 0.02, respectively. The range of genomic predicted transmitted abilities (gPTAs) of training population (-0.94 to 0.75) was higher compared with the range of gPTAs (-0.82 to 0.73) of different groups of prediction population. Average reliability of breeding values from training population was 58% and that of prediction population was 39%. The genomic prediction of RFI provided new tools to select for feed efficiency of heifers. Future research should be directed to find the relationship between RFI of heifers and cows, to select individuals based on their lifetime production efficiencies.

Estimation of dry matter intake by lambs managed on different leaves allowance of Brachiaria-grass using internal and external markers

ABSTRACT It aimed to estimate the forage intake by lambs managed in different leaf allowances on Brachiaria-grass using internal and external markers. Forty-eight Texel lambs with 17.52 ± 3.48 kg were evaluated which were 12 testers and 36 regulators. The lambs were kept in twelve paddocks, subdivided into four treatments based on green leaf allowance (60, 75, 90, and 105 g/kg of BW). The experimental design was completely randomised. To estimate dry matter intake (DMI), the following markers were used: indigestible dry matter (iDM); indigestible neutral detergent fibre (iNDF); indigestible acid detergent fibre (iADF); lignin in indigestible acid detergent (iADL) and titanium dioxide (TiO2). Comparisons between intake markers were made using Chromic Oxide (Cr2O3) as a control marker. The leaves allowance tested did not alter the voluntary intake by lambs kept in Brachiaria-grass pasture. Comparison between the control intake marker (Cr2O3) and the other markers by Dunnett's test showed that, regardless of green leaf allowance, the Cr2O3, TiO2 and iADL markers provided similar estimates of DMI, but with lower values than the other markers. External marker TiO2 and the internal iADL were demonstrated to be more suitable for estimating the dry matter intake in grazing lambs regardless of the leaf allowance.

Influence of pre-grazing herbage mass on bite mass, eating behaviour, and dairy cow performance on pasture.

Knowledge about individual daily herbage dry matter (DM) intake (DMI) helps identifying efficient dairy cows and adapting supplementation better to herbage intake and nutrient requirements of grazing dairy cows. With the aid of behavioural characteristics, raw data recorded with the RumiWatch (RW) system and processed with the RW converter 0.7.3.31 (C31), estimation of herbage DMI may be possible. First, C31, which allows differentiation of prehension bites and mastication chews, was validated through direct observation of behavioural characteristics and compared to the previous RW converter 0.7.3.11 (C11). Further, the influence of a low and high pre-grazing herbage mass (HM), with the same target herbage allowance (HA), on bite mass, DMI, number of prehension bites, and milk production was investigated. In total, 24 lactating Holstein cows were pairwise allotted to one of two HM treatments. The cows received a new pasture paddock twice per day with a daily target HA of 22 kg DM per cow/day. On average, low HM (LHM) and high HM (HHM) paddocks had an HM of 589 and 2288 kg DM/ha, respectively, above 6.7 click units (1 CU = 0.5 cm). Overall, LHM cows produced 2.7 kg/day more milk and 2.5 kg/day more energy-corrected milk, had the same herbage DMI and a similar prehension bite mass. The averaged bite mass per week was 0.49 g DM/bite (LHM) or 0.47 g DM/bite (HHM), respectively. A longer eating time (617 vs. 559 min/day) and a shorter rumination time (297 vs. 365 min/day) were observed for the LHM cows compared with the HHM cows. The validation of the RW showed similar results for C11 and C31 apart from prehension bites, where C31 showed a mean absolute deviation of 12.4%. Pre-grazing HM had no effect on relevant behavioural characteristics for prospective intake estimation, namely, bite mass and number of prehension bites.

Comparisons between intake values observed and predicted by nutritional systems for confined nellore cattle

This study compared the dry matter intake (DMI) of Nellore heifers and bulls in the feedlot, predicted by the BR-Corte (2010 and 2016) and NRC (2000) nutritional systems. Hence, two experiments were conducted in a completely randomized design. The first one used 47 Nellore bulls, not castrated, with an average initial weight of 413 kg, and 19 months of age. The second experiment used 24 Nellore heifers with an average initial weight of 300 kg and 23 months of age. The accuracy and approximation of the DMI estimates by the nutritional systems were adjusted with the simple linear regression model and the root mean square error of prediction (RMSEP). The DMI was 8.06 kg day-1 for Nellore heifers and 11.54 kg day-1 for bulls, which are higher than the values ​​ predicted by the nutritional systems. The NRC (2000) and BR-Corte (2010 and 2016) underestimated DMI in 20.84, 20.09, and 19.35% for heifers and 28.07, 16.20, and 11.78% for bulls, respectively. It was concluded that the BR-Corte 2010 and 2016 were the most suitable models to estimate the DMI of Nellore heifers and bulls for higher precision and accuracy.

Temporal variability in automatically-collected liveweight data affects estimation of dry matter intake of dairy cows

Daily liveweight monitoring can be done using an automated weighing system. However, liveweight of dairy cows is extremely variable between and within individual cows and days due to various factors. Large variation in daily liveweight changes can be seen when measured at different times of the day, and that may affect the estimation of daily energy required by cows, and directly affect the estimation of dry matter intake. The objectives of the present study were to quantify the variation in cow’s liveweight automatically recorded in the morning and afternoon, and subsequently demonstrate the differences in estimates of dry matter intake. Morning and afternoon liveweight data from 64 primiparous and multiparous lactating Holstein-Friesian cows were used in this study. Two analyses were performed, without (raw) and with smoothing liveweight data using a spline function to minimize measurement errors and reduce the variability in liveweight over time. Dry matter intake for individual animals was estimated using back-calculation of the energy requirement from liveweight, liveweight change, milk production and milk composition. Results showed that, on average, cows were significantly heavier in the afternoon (Mean ± SD = 614.3 ± 78.20 kg) compared to morning (593.2 ± 76.08 kg; P < 0.01). In terms of variability, the residual standard deviation indicated that the afternoon liveweight had more fluctuations (P < 0.01) over the time compared to those recorded in the morning. The estimation of both individual liveweight change and dry matter intake using morning and afternoon liveweight in their raw form showed unrealistic values ranging from −110.0 to +96.0 and −337.57 to + 399.74 kg/day, respectively. The smoothing of automatically-collected liveweight data substantially reduced the variability of liveweight, liveweight change and dry matter intake. The estimated dry matter intake ranged between 1 and 5% of liveweight/day when calculated using adjusted liveweight values. Based on the residual standard deviation for estimated dry matter intake, we concluded that a single liveweight measurement per cow in the morning is adequate to adjust the automatically-collected liveweight to reduce variability in the estimated intake.

Diet, marker and fecal sampling method interactions with internal and external marker pairs when estimating dry matter intake in beef cattle

The use of markers is currently the most used technique for estimating feed intake in production animals when direct measurement is not possible. Twelve growing Nellore bulls were used in a crossover design aiming to evaluate the accuracy of internal markers to estimate dry matter digestibility (DMD), the accuracy of external markers to estimate fecal output (FO), and the combination of internal and external markers to estimate dry matter intake (DMI) at different fecal sampling procedures. Animals received one of the four diets which varied in forage source (corn silage or Tifton-85 hay) and forage to concentrate ratio (F:C; 60:40 or 40:60) in a 2 × 2 factorial arrangement. The internal markers, acetyl bromide lignin (ABL), indigestible aNDFom (iNDF), indigestible ADFom (iADF) and cutin, are naturally present in the feedstuffs and the external markers, Cr2O3 and TiO2, were daily fed to the animals. Three fecal sampling procedures were tested: bulk 72-h (continuous feces collection for 72 h), grab 9 × 3 (sample collection every 9 h over 3-d period), and grab 4 × 4 (4 fecal samples collected during daylight hours over 4-d period). None of the internal or external markers evaluated had complete fecal recovery (FR), being necessary to establish the FR of the markers in order to obtain correct estimates. The FR of ABL, iNDF and iADF were close to 100% when animals received a hay-based diets but close to 50% when feeding a corn silage-based diets, regardless the F:C ratio of the diet. However, ABL produced accurate DMD estimates only with both grab sampling procedures across all diets, while iNDF and iADF produced accurate DMD estimates across all sampling procedures and diets. Cutin failed to produce accurate DMD consistently. Both external markers produced accurate FO under both grab sampling procedures, except for TiO2 when the grab 4 × 4 sampling procedure was performed for animals receiving the diet with high silage proportion. In general, the grab sampling procedures yielded accurate DMD, FO and DMI estimates, which were similar or better than bulk 72 h sampling procedure. The combination of internal and external markers to estimate DMI produced satisfactory and accurate results, particularly when Cr2O3 or TiO2 was paired with iNDF or iADF under both grab sampling procedures. The grab sampling procedures can replace TFC, opening new possibilities for collectively housed animals.

Dairy cattle nutrition and feed calculator-an android application.

The dairy farmers in the tropical region have limitations regarding "feedstuffs" available. A software program is required to meet the nutrient requirement with the available feeds at the cheapest cost. There are many techniques/software that are implemented to meet dairy animal nutrient requirements. However, there are no specific techniques that are appropriate for formulating rations for dairy cattle in the least cost manner. An android application Dairy Cattle Nutrition and Feed Calculator (DCN & FC) is developed with an aim of least-cost feed balancing system which will help a farmer to choose feeds to meet the nutrient requirement set for ration. The quantity of feed, fed to the cattle to meet the required needs, is determined by nutrient content, dry matter intake (DMI), crude protein (CP) and total digestible nutrients (TDN) of the feed. This program works in three phases. The first phase consists of creating equations for the estimation of DMI, TDN and CP based on the nutrient tables provided by ICAR (2013) for different categories of cattle such as Milch cattle, dry cattle, male calves, female calves and pre-ruminant animals for different physiological stages such as body weight, average daily gain, fat and milk, using regression analysis. The second phase is the development of a linear programming (LP) model to solve different cases of nutrient requirements for least cost. The final phase is the use of the android framework that uses linear programming to suggest sufficient feed that meets the nutrient requirements. The developed application is user friendly and available in different languages. With the combination of linear programming and regression analysis, the ration can be effectively formed using available feeds, and control the cost of the feeds.

Evaluation of the n-alkane technique for estimating the individual intake of dairy cows consuming diets containing herbage and a partial mixed ration

Estimation of dry matter intake (DMI) using the n-alkane technique was evaluated in lactating dairy cows fed fresh herbage and a partial mixed ration (PMR). Four dietary treatments were investigated in a 2 × 2 factorial experiment using 16 Holstein-Friesian dairy cows. Dietary treatments were combinations of low and high amounts of fresh herbage (8 or 14 kg DM/cow per day) and PMR supplement (6 or 12 kg DM/cow per day). The pre-experimental period was 14 days followed by a 10-day experimental period. Cows were housed in individual metabolism stalls to allow for accurate measurement of DMI and total fecal output. Fecal n-alkane recovery rates were calculated to determine the most accurate corrections for incomplete fecal n-alkane recovery. The n-alkane technique accurately estimated DMI when corrected for incomplete fecal recovery using both published recovery rates and recovery rates calculated in this experiment. The most accurate application of recovery rates was with those calculated for each combination of dietary treatments, compared with using an average recovery rate. This research has important implications for the future use of the n-alkane technique, especially in PMR feeding systems. The discrepancy between estimated (when treatment recovery rates were applied) and measured herbage DMI increased with the amount of herbage offered but was not affected by amount of PMR. It was also found that the recovery rates of all natural n-alkanes increased as the amount of herbage increased. This research demonstrates that the n-alkane technique can be used to accurately estimate individual cow intake when fresh herbage and PMR are offered separately, evidenced by strong Lin’s concordance estimates.

Effect of Condensed Tannins in Lotus uliginosus cv. E-Tanin and/or Extracts of Quebracho and Chestnut in Carcass and Lamb Meat

The effect of condensed tannins (CT) in the pasture (Lotus uliginosus cv. E-Tanin) and/or quebracho and chestnut extracts on lamb carcass and meat quality was evaluated. Two experiments were conducted in consecutive years with 60 crossbreed lambs each year. The animals were stratified by live weight (LW) and randomly assigned to the treatments: white clover grazing (WC); WC plus concentrate supplementation (WC + C); WC plus concentrate supplementation and quebracho and chestnut extract (WC + C + CT); Lotus uliginosus grazing (E-Tanin); E-Tanin plus concentrate supplementation (E-Tanin + C); E-Tanin plus concentrate supplementation plus quebracho and chestnut extract (E-Tanin + C + CT); with two repetitions each year. Pasture type affected loin weight, which was greater (P &lt; 0.05) in lambs grazing WC than E-Tanin. Meat color redness (a*) was greater (P &lt; 0.05) in supplemented treatments with C (with or without CT) than those exclusively grazing. Intramuscular fat content was not affected (P &gt; 0.05) by the pasture, the supplementation, or the year. The proportion of dihomo-gamma-linolenic acid (C20:3-n6) was greater (P &lt; 0.05) in lambs grazing E-Tanin compared to WC. Supplementation affected (P &lt; 0.05) the saturated (SFA) and monounsaturated fatty acids (MUFA) proportions, and the omega 6:omega 3 ratio. Estimation of dry matter intake under grazing conditions would allow a better understanding of the effect of CT on carcass and meat quality.

Herbage dry matter intake estimation of grazing dairy cows based on animal, behavioral, environmental, and feed variables

Information about the individual herbage DMI (HDMI) of grazing dairy cows is important for an efficient use of pasture herbage as an animal feed with a range of benefits. Estimating HDMI, with its multifaceted influencing variables, is difficult but may be attempted using animal, performance, behavior, and feed variables. In our study, 2 types of approaches were explored: 1 for HDMI estimation under a global approach (GA), where all variables measured in the 4 underlying experiments were used for model development, and 1 for HDMI estimation in an approach without information about the amount of supplements fed in the barn (WSB). The accuracy of these models was assessed. The underlying data set was developed from 4 experiments with 52 GA and 50 WSB variables and one hundred thirty 7-d measurements. The experiments differed in pasture size, herbage allowance, pregrazing herbage mass, supplements fed in the barn, and sward composition. In all the experiments, cow behavioral characteristics were recorded using the RumiWatch system (Itin and Hoch GmbH, Liestal, Switzerland). Herbage intake was estimated by applying the n-alkane method. Finally, HDMI estimation models with a minimal relative prediction error of 11.1% for use under GA and 13.2% for use under WSB were developed. The variables retained for the GA model with the highest accuracy, determined through various selection steps, were herbage crude protein, chopped whole-plant corn silage intake in the barn, protein supplement or concentrate intake in the barn, body weight, milk yield, milk protein, milk lactose, lactation number, postgrazing herbage mass, and bite rate performed at pasture. Instead of the omitted amounts of feed intake in the barn and, due to the statistical procedure for model reduction, the unconsidered variables postgrazing herbage mass and bite rate performed at pasture, the WSB model with the highest accuracy retained additional variables. The additional variables were total eating chews performed at pasture and in the barn, total eating time performed at pasture, number of total prehension bites, number of prehension bites performed at pasture, and herbage ash concentration. Even though behavioral characteristics alone did not allow a sufficiently accurate individual HDMI estimation, their inclusion under WSB improved estimation accuracy and represented the most valid variables for the HDMI estimation under WSB. Under GA, the inclusion of behavioral characteristics in the HDMI estimation models did not reduce the root mean squared prediction error. Finally, further adaptation, as well as validation on a more comprehensive data set and the inclusion of variables excluded in this study such as body condition score or gestation, should be considered in the development of HDMI estimation models.

Developing and validating a model to predict the dry matter intake of grazing lactating beef cows

Current techniques for measuring the dry matter intake (DMI) of grazing lactating beef cows are invasive, time consuming and expensive making them impractical for use on commercial farms. This study was undertaken to explore the potential to develop and validate a model to predict DMI of grazing lactating beef cows, which could be applied in a commercial farm setting, using non-invasive animal measurements. The calibration dataset used to develop the model was comprised of 94 measurements recorded on 106 beef or beef–dairy crossbred cows (maternal origin). The potential of body measurements, linear type scoring, grazing behaviour and thermal imaging to predict DMI in combination with known biologically plausible adjustment variables and energy sinks was investigated. Multivariable regression models were constructed for each independent variable using SAS PROC REG and contained milk yield, BW, parity, calving day and maternal origin (dairy or beef). Of the 94 variables tested, 32 showed an association with DMI (P < 0.25) upon multivariable analysis. These variables were incorporated into a backwards linear regression model using SAS PROC REG. Variables were retained in this model if P < 0.05. Five variables; width at pins, full body depth, ruminating mastications, central ligament and rump width score, were retained in the model in addition to milk yield, BW, parity, calving day and maternal origin. The inclusion of these variables in the model increased the predictability of DMI by 0.23 (R2 = 0.68) when compared to a model containing milk yield, BW, parity, calving day and maternal origin only. This model was applied to data recorded on an independent dataset; a herd of 60 lactating beef cows two years after the calibration study. The R2 for the validation was 0.59. Estimates of DMI are required for measuring feed efficiency. While acknowledging challenges in applicability, the findings suggest a model such as that developed in this study may be used as a tool to more easily and less invasively estimate DMI on large populations of commercial beef cows, and therefore measure feed efficiency.

Model Predicted DMI, Nitrogen (N) Excretion and N Use Efficiency Utilizing Plasma Urea Nitrogen (PUN) Versus Values Estimated in Conjunction with Viable Dry Matter Intake Estimates in Lambs Grazing Pasture

Model Predicted DMI, Nitrogen (N) Excretion and N Use Efficiency Utilizing Plasma Urea Nitrogen (PUN) Versus Values Estimated in Conjunction with Viable Dry Matter Intake Estimates in Lambs Grazing Pasture

Evaluation of the n-alkane technique for estimating herbage dry matter intake of dairy cows offered herbage harvested at two different stages of growth in summer and autumn

The n-alkane technique for estimating herbage dry matter intake (DMI) of dairy cows was investigated in this experiment. Eight Holstein-Friesian dairy cows were offered perennial ryegrass ad libitum that had been harvested at two different herbage masses and during two different seasons, in order to assess the effect of herbage mass and season on the accuracy of the n-alkane technique. Two pre-harvested herbage mass treatments (low, target 1500 kg DM/ha versus high, target 4000 kg DM/ha, measured above 4 cm), were investigated in a crossover factorial arrangement within each of two seasons (summer versus autumn), in Ireland. Each season consisted of two periods, each 12 days in length. Cows were housed in individual metabolism stalls to allow for accurate determination of measured DMI. Herbage DMI was estimated, with the n-alkane technique, by dosing cows twice daily with a C32 n-alkane. Pre-harvest herbage mass and season did not affect the n-alkane estimated DMI, although lack of season and herbage mass effects may have been masked by variation that occurred between swards within the same herbage mass and season. However, there were a number of differences between summer and autumn in the fecal recovery rates of a number of n-alkanes suggesting that the effect of season requires further investigation prior to the application of recovery rates from literature values when investigating diet selection and botanical composition. Overall, the n-alkane technique provided good estimates of DMI; the discrepancy had a standard deviation due to sward of 1.2 and 1.0 kg DM/cow per day, and hence potential bias of up to twice this, and a measurement error standard deviation of 1.3 and 1.0 kg DM/cow per day, for the C33/C32 and C31/C32 n-alkane pair methods respectively. Two n-alkane pairs were tested, and C33/C32 n-alkane provided the most precise estimates of DMI, compared with the C31/C32 n-alkane pair. This research provides some strong evidence for future use of the n-alkane technique including that the accuracy of the technique has not been influenced by contemporary changes to herbage management, is not affected by seasonal changes, and overall is an accurate and precise technique for estimating DMI.

Evaluating internal and external markers versus fecal sampling procedure interactions when estimating intake in dairy cows consuming a corn silage-based diet

Feed intake assessment is a valuable tool for herd management decisions. The use of markers, either internal or external, is currently the most used technique for estimating feed intake in production animals. The experiment used 10 multiparous Holstein cows fed a corn silage-based diet, with 55:45 forage-to-concentrate ratio, the average fecal recovery (FR) of TiO2 was higher than FR of Cr2O3, and both FR were more than unity. With internal markers, acetyl bromide lignin and cutin FR were lower than unity, and average FR for indigestible neutral detergent fiber (iNDF) and indigestible acid detergent fiber (iADF) was 1.5. The FR was unaffected by the fecal sampling procedure and appears to be an intrinsic property of each molecule and how it interacts with digesta. Of the 2 external markers, only Cr2O3 produced accurate fecal output (FO) estimates and the same happened to dry matter digestibility (DMD) when iNDF and iADF were used. Estimates for DMD and FO were affected by sampling procedure; 72-h bulk [sub-sample from total feces collection (TFC)] sampling consistently produced accurate results. The grab (sub-samples taken at specific times during the day) sampling procedures were accurate when using either of the indigestible fibers (iNDF or iADF) to estimate DMD. However, grab sampling procedures can only be recommended when concomitant TFC is performed on at least one animal per treatment to determine FR. Under these conditions, Cr2O3 is a suitable marker for estimating FO, and iNDF and iADF are adequate for estimating DMD. Moreover, the Cr2O3+iADF marker pair produces accurate dry matter intake estimates and deserves further attention in ruminant nutrition studies. The method of dosing the external markers is extremely important and greatly affects and determines results. Whichever the method, it must allow the animals to display normal feeding behavior and not affect performance. The grab sampling procedures can replace TFC (once FR is established), which may open new possibilities for pasture-based or collectively housed animals.

Impact of dynamics of faecal concentrations of plant and synthetic n-alkanes on their suitability for the estimation of dry matter intake and apparent digestibility in horses – CORRIGENDUM

Impact of dynamics of faecal concentrations of plant and synthetic n-alkanes on their suitability for the estimation of dry matter intake and apparent digestibility in horses – CORRIGENDUM - Volume 155 Issue 9

Optimising natural 13C marker based pasture intake estimates for cattle using a genetic algorithm approach

The sensitivity of pasture intake estimates obtained from using 13C as a marker to differences in assumed diet composition and 13C diet-faecal discrimination was studied. Angus stud heifers grazed a silver grass, perennial ryegrass, bent grass and yorkshire fog pasture. The individual heifers were fed controlled and monitored daily amounts of maize and faecal samples were taken and analysed to estimate dry matter intake (DMI) and DMI/liveweight (LW). Daily methane production was also measured. Monte Carlo simulations using a uniform distribution of diet composition and an extreme value distribution for the 13C diet-faecal discrimination found that the DMI/LW ratio was twice as sensitive to assumed diet composition (and hence pasture 13C) than to the diet-faeces discrimination factor. DMI estimates would be useful for ranking animals on DMI intake alone as the rank correlations for DMI estimated using different input assumptions were high. A genetic algorithm approach was helpful as a means of determining the optimum diet selection or plant proportions to use for each animal and the diet-faecal discrimination to use when uncertainty exists as to their true values, which may often be the case. Some animals had non-credible DMI/LW values when using standard calculation methods. There are no definitive goals or constraints to use but careful choice of the range of individual DMI/LW values set as a hard constraint enabled credible DMI/LW values for all animals to be obtained when using a genetic algorithm approach.