Predicting dry matter intake in beef cattle

Technology that facilitates estimations of individual animal dry matter intake (DMI) rates in group-housed settings will improve production and management efficiencies. Estimating DMI in pasture settings or facilities where feed intake cannot be monitored may benefit from predictive algorithms that use other variables as proxies. This study examined the relationships between DMI, animal performance, and environmental variables. Here we determined whether a machine learning approach can predict DMI from measured water intake variables, age, sex, full body weight, and average daily gain (ADG). Two hundred and five animals were studied in a drylot setting (152 bulls for 88 d and 53 steers for 50 d). Collected data included daily DMI, water intake, daily predicted full body weights, and ADG using In-Pen-Weighing Positions and Feed Intake Nodes. After exclusion of 26 bulls of low-frequency breeds and one severe (>3 standard deviations) outlier, the final number of animals used for modeling was 178 (125 bulls, 53 steers). Climate data were recorded at 30-min intervals throughout the study period. Random Forest Regression (RFR) and Repeated Measures Random Forest (RMRF) were used as machine learning approaches to develop a predictive algorithm. Repeated Measures ANOVA (RMANOVA) was used as the traditional approach. Using the RMRF method, an algorithm was constructed that predicts an animal's DMI within 0.75 kg. Evaluation and refining of algorithms used to predict DMI in drylot by adding more representative data will allow for future extrapolation to controlled small plot grazing and, ultimately, more extensive group field settings.

Effects of diet on feed intake, weight change, and gas emissions in beef cows

The objective of this study was to examine the effects of diet energy density on ranking for dry matter intake (DMI), residual feed intake (RFI), and greenhouse gas emissions. Forty-two mature, gestating Angus cows (600 ± 69 kg body weight [BW]; body condition score [BCS] 5.3 ± 1.1) with a wide range in DMI expected progeny difference (-1.38 to 2.91) were randomly assigned to two diet sequences; forage then concentrate (FC) or concentrate then forage (CF). The forage diet consisted of long-stem native grass hay plus protein supplement (HAY; 1.96 Mcal ME/kg DM). The concentrate diet consisted of 35% chopped grass hay and 65% concentrate feeds on a dry matter basis (MIX; 2.5 Mcal ME/kg DM). The GreenFeed Emission Monitoring system was used to determine carbon dioxide (CO2), oxygen (O2), and methane (CH4) flux. Cow performance traits, ultrasound back fat and rump fat, feed DMI, and gas flux data were analyzed in a crossover design using a mixed model including diet, period, and sequence as fixed effects and pen and cow within sequence as random effects. For all measured traits excluding DMI, there was a diet × sequence interaction (P < 0.05). The correlation between MIX and HAY DMI was 0.41 (P = 0.067) and 0.47 (P = 0.03) for FC and CF sequences, respectively. There was no relationship (P > 0.66) between HAY and MIX average daily gain (ADG), regardless of sequence. Fifty-seven percent of the variation in DMI was explained by metabolic BW, ADG, and BCS for both diets during the first period. During the second period, the same three explanatory variables accounted for 38% and 37% of the variation in DMI for MIX and HAY diets, respectively. The negative relationship between BCS and DMI was more pronounced when cows consumed the MIX diet. There was no relationship between MIX and HAY RFI, regardless of sequence (P > 0.18). During the first period, correlations for CO2, CH4, and O2 with MIX DMI were 0.69, 0.81, and 0.56 (P ≤ 0.015), respectively, and 0.76, 0.74, and 0.64 (P < 0.01) with HAY DMI. During the second period, correlations for CO2, CH4, and O2 with MIX DMI were 0.62, 0.47, and 0.56 (P ≤ 0.11), respectively. However, HAY DMI during the second period was not related to gas flux (P > 0.47). Results from this experiment indicate that feed intake of two energy-diverse diets is moderately correlated while ADG while consuming the two diets is not related. Further experimentation is necessary to determine if gas flux data can be used to predict feed intake in beef cows.

Understanding intake on pastures: how, why, and a way forward

An assessment of dietary intake is a critical component of animal nutrition. Consumption of feed resources is the basis upon which feeding strategies and grazing management are based. Yet, as far back as 1948, researchers have lauded the trials and tribulations of estimation of the phenomenon, especially when focused on grazing animals and pasture resources. The grazing environment presents a unique situation in which the feed resource is not provided to the animal but, rather, the animal operates as the mechanism of harvest. Therefore, tools for estimation must be developed, validated, and applied to the scenario. There are a plethora of methods currently in use for the estimation of intake, ranging from manual measurement of herbage disappearance to digital technologies and sensors, each of which come with its share of advantages and disadvantages. In order to more firmly grasp these concepts and provide a discussion on the future of this estimation, the Forages and Pastures Symposium at the 2020 ASAS-CSAS-WSASAS Annual Meeting was dedicated to this topic. This review summarizes the presentations in that symposium and offers further insight into where we have come from and where we are going in the estimation of intake for grazing livestock.

The Effect of Beef Production System on the Health, Performance, Carcass Characteristics, and Meat Quality of Holstein Bulls

The aim of this study was to evaluate the effect of production system on the health, performance, carcass characteristics, and meat quality of autumn born (AB) and spring born (SB) Holstein bulls. The study involved a total of 224 Holstein bulls and was conducted over two years (2017/18, 2018/19). The four production system treatments differed during the grower period and consisted of: (i) grazed with no concentrate supplementation (G), (ii) grazed with 2 kg concentrate supplementation per day (G2), (iii) grazed with ad libitum access to concentrates (GA) and (iv) housed with ad libitum access to concentrates and grass silage (HA). All bulls were finished on ad libitum concentrates and grass silage and were slaughtered at a mean age of 15.5 months. Total grower dry matter intake (DMI) (p < 0.001) and total finishing DMI (p < 0.001) differed between production systems for both AB and SB bulls, with that of GA bulls being the greatest in both cases. Average daily gain (ADG) during the grower period was greatest (p < 0.001) for the HA production system in the AB bulls and the GA and HA production systems for the SB bulls. However, during the finishing period, G bulls had the greatest (p < 0.001) ADG of the AB bulls, while that of the SB bulls was from the G2 production system (p < 0.001). For both AB and SB, bulls on the GA and HA production systems produced heavier cold carcass weights than the G and G2 bulls (p < 0.001). There was no significant difference (p > 0.05) in health, carcass conformation, fat classification, or meat quality between production systems.

Sheep in Species-Rich Temperate Grassland: Combining Behavioral Observations with Vegetation Characterization

Simple Summary

Grasslands cover much of the world, and numerous people depend on the livestock that graze them for their livelihoods. These areas must be properly managed as they are often ecologically fragile. Therefore, how the foraging animal interacts with its environment needs to be understood. These interactions have mostly been studied in highly productive intensively managed and improved grasslands, which typically have only a limited number of commercially developed plant varieties. Little is known about how animals interact with less intensively managed, species-rich grasslands which are often of conservation significance because of their biodiversity. In this preliminary study, we have used video technology to investigate responses of sheep to the vegetation of unimproved grassland in Estonia. We classified the vegetation with a methodology that is standard in plant ecology but which has not been extensively applied in animal behavior. We also demonstrate the use of a novel procedure for quantifying foraging behavior. This combination of methodologies will enable the characterization of individual animal variations in these important behaviors, which could provide a basis for the rational design of sustainable grassland management systems.

Abstract

Foraging behavior of livestock in species-rich, less intensively managed grassland communities will require different methodologies from those appropriate in floristically simple environments. In this pilot study on sheep in species-rich grassland in northern Estonia, foraging behavior and the plant species of the immediate area grazed by the sheep were registered by continually-recording Go-Pro cameras. From three days of observation of five sheep (706 animal-minutes), foraging behavior was documented. Five hundred and thirty-six still images were sampled, and a plant species list was compiled for each. Each plant species was assigned a score indicating its location, in the ecophysiological sense, on the main environmental gradient. The scores of the plant species present were averaged for each image. Thus, the fine structure of foraging behavior could be studied in parallel with the vegetation of the precise area being grazed. As expected, there was considerable individual variation, and we characterized foraging behavior by quantifying the patterns of interspersion of grazing and non-grazing behaviors. This combination of behavior recording and vegetation classification could enable a numerical analysis of the responses of grazing livestock to vegetation conditions.

Energy relations in cattle can be quantified using open-circuit gas-quantification systems

This study was conducted to evaluate the relationships between metabolisable energy (ME) intake and outputs of methane (CH4), rumen-derived carbon dioxide (rCO2), lung-derived carbon dioxide (lCO2), and total carbon dioxide output (tCO2) measured using an open-circuit gas-quantification system (GQS). Three trials were conducted to produce a wide range of energy intake and gas emissions to allow relationships between gas outputs and ME intake to be quantified. Gas emissions and ME intake were measured in eight Angus steers (455 ± 24.6 kg initial bodyweight; Trials 1 and 2), and in eight pregnant Angus heifers (503 ± 22.0 kg initial bodyweight; 5 months pregnant; Trial 3). Animals were fed twice daily to allow ad libitum intake in Trial 1, whereas in Trials 2 and 3, feed intake was restricted and energy density was varied to provide a wide range of ME intakes. Animals were allocated to individual pens during a 20-, 19- and 15-day experimental periods, and total faecal output was measured for the last 8, 4 and 4 days in Trials 1, 2 and 3 respectively. Gas emissions were measured for 16, 8 and 8 days after the adaptation period (4, 11 and 7 days) and each animal was allowed to visit the GQS every 2 h. Total CO2 in breath (tCO2) was separated into CO2 arising from rumen fermentation (rCO2) and CO2 in expired air from the lungs (lCO2) by manually identifying the eructations from normal breaths using the GQS gas-output trace. All CO2 outputs (lCO2, rCO2 and tCO2) were highly correlated with each other (r = 0.74–0.99; P < 0.01). Measurement of CO2 output was more repeatable with fewer days of measurement than was CH4 output. Metabolisable-energy intake was closely related to all three measures of CO2 output (rCO2, r = 0.69, P < 0.001; lCO2, r = 0.70, P < 0.001; and tCO2, r = 0.73, P < 0.001). Heat production was estimated from lCO2 output by assuming a value of 0.85 for the respiratory quotient of metabolised products. The heat production estimated at the extrapolated zero ME intake (0.52 MJ/kg0.75) was 60% higher than previous estimates of fasting heat production in cattle. However, our estimate was made under non-fasting, non-sedentary, non-thermoneutral conditions, so it may be a realistic estimate of maintenance energy requirement excluding heat increment of feeding. In conclusion, the open-circuit GQS can be used to provide estimates of the ME intake and heat production of cattle, and, as such, provides a valuable opportunity to describe the energy relations and efficiency of beef cattle in the field, with minimal interference to normal grazing patterns and behaviour.

One-hour portable chamber methane measurements are repeatable and provide useful information on feed intake and efficiency

Feed intake (FI), live weight (LW), and ADG were recorded over 31 d in ninety-six 12-month-old ewes (progeny of 4 sires) given ad libitum access to chaffed lucerne/cereal hay. Methane (CH) and CO emissions of each ewe were measured for 40 to 60 min in portable accumulation chambers (PAC) and in respiration chambers (RC) over 22 h. Testing in RC increased the variability of FI on the test day and depressed the amount eaten from an average of 1,384 to 1,062 g/d; FI depression increased by 0.63 ± 0.24 percentage points for every kilogram of additional LW. Repeatabilities of PAC measurements were 0.76 (CH) and 0.81 (CO). After adjusting for LW and ADG, repeatabilities were 0.47 (PAC CH) and 0.43 (PAC CO). Daily FI measurements had similar repeatability (0.76 before and 0.42 after adjustment for LW and ADG). The PAC measurements were highly correlated with mean 31-d FI ( = 0.81 for both CH and CO). After adjustment for LW and ADG, PAC measurements were moderately correlated with residual feed intake (RFI; = 0.37 for CH, 0.31 for CO). The CH:CO ratio was also significantly correlated with mean 31-d FI ( = 0.52). After most of the ewes had given birth and raised lambs, repeat PAC measurements were available for 91 of the ewes at 2 years of age (with ad libitum access to the same feed). Correlations with the 2012 PAC measurements were 0.64 (CH) and 0.75 (CO). After adjusting 2014 PAC measurements for LW, correlations with RFI in 2012 were 0.34 (CH) and 0.33 (CO), with a clear relationship between sire means for RFI in 2012 and PAC CH adjusted for LW in 2014. These results suggest that PAC tests under similar feeding conditions are repeatable over an extended time period and can provide useful information on FI and feed efficiency as well as methane emissions. Analyses of RC measurements might need to consider FI depression.

Daily methane emissions and emission intensity of grazing beef cattle genetically divergent for residual feed intake

As daily methane production (DMP; g CH4/day) is strongly correlated with dry matter intake (DMI), the breeding of cattle that require less feed to achieve a desired rate of average daily gain (ADG) by selection for a low residual feed intake (RFI) can be expected to reduce DMP and also emission intensity (EI; g CH4/kg ADG). An experiment was conducted to compare DMP and EI of Angus cattle genetically divergent for RFI and 400-day weight (400dWT). In a 6-week grazing study, 64 yearling-age cattle (30 steers, 34 heifers) were grazed on temperate pastures, with heifers and steers grazing separate paddocks. Liveweight (LW) was monitored weekly and DMP of individual cattle was measured by a GreenFeed emission monitoring unit in each paddock. Thirty-nine of the possible 64 animals had emission data recorded for 15 or more days, and only data for these animals were analysed. For these cattle, regression against their mid-parent estimated breeding value (EBV) for post-weaning RFI (RFI-EBV) showed that a lower RFI-EBV was associated with higher LW at the start of experiment. Predicted dry matter intake (pDMI), predicted DMP (pDMP) and measured DMP (mDMP) were all negatively correlated with RFI-EBV (P < 0.05), whereas ADG, EI, predicted CH4 yield (pMY; g CH4/kg DMI) were not correlated with RFI-EBV (P > 0.1). Daily CH4 production was positively correlated with animal LW and ADG (P < 0.05). The associations between ADG and its dependent traits EI and pMY and predicted feed conversion ratio (kg pDMI/kg ADG) were strongly negative (r = –0.82, –0.57 and –0.85, P < 0.001) implying that faster daily growth by cattle was accompanied by lower EI, MY and feed conversion ratio. These results show that cattle genetically divergent for RFI do not necessarily differ in ADG, EI or pMY on pasture and that, if heavier, cattle with lower RFI-EBV can actually have higher DMP while grazing moderate quality pastures.

Use of body linear measurements to estimate liveweight of crossbred dairy cattle in smallholder farms in Kenya

Body linear measurements, and specifically heart girth (HG), have been shown to be useful predictors of cattle liveweight. To test the accuracy of body linear measurements for predicting liveweight, crossbred dairy cattle of different genotypes were measured and weighed. A total of 352 mature cows and 100 heifers were weighed using an electronic weighing scale and measurements of HG, body length, height at withers were taken using an ordinary measuring tape and body condition scored (BCS) using a five-point scale. The animals were grouped according to genotype and age. Genotype classification was undertaken from farmer recall and by visual appraisal as 40–60, 61–80 or 81–100 % exotic (non-indigenous). Age classification was simply as mature cows or heifers. Liveweight of the animals ranged from 102 to 433 kg. Liveweight was strongly correlated with HG (r = 0.84) and body condition scores (r = 0.70) and moderately correlated with body length (r = 0.64) and height at withers (0.61). Regressing LW on HG measurements gave statistically significant (P < 0.01) equations with R² ranging from of 0.53 to 0.78 and residual standard deviation ranging from 18.11 to 40.50 kg. The overall model developed (adjusted R² = 0.71) had a prediction error of 26 kg (or 11 % of the mean) and predicted LW of over 95 % of crossbred dairy cattle in the range of 100–450 kg, regardless of age and breed group. Including BCS in the model slightly improved the model fit but not the prediction error. It was concluded that the model can be useful in making general management decisions in smallholder farms.

A meta-analysis of nutrient intake, feed efficiency and performance in cattle grazing on tropical grasslands

It is essential to quantify the potential of tropical grasslands to allow significant feed efficiency for grazing livestock in controlled conditions such as at pasture. We conducted a quantitative analysis of published studies reporting the experimental results of average daily gains (ADG) and diet characteristics obtained specifically under grazing conditions (17 publications and 41 experiments), which have been less studied compared with controlled conditions in stalls. The database was analyzed to determine the average and range of values obtained for ADG (g/kg BW), dry matter digestibility, intake (DMI) and digestible DMI (DDMI, g/kg BW) and feed conversion efficiencies (FCE), as well as to predict the response of these parameters to the main strategies investigated in the literature - that is, mainly the stocking rate (SR) and the concentrate intake (CI). The ADG reached 1.2 kg BW per day and was directly linked to DDMI (ADG=-1.63+0.42 DDMI -0.0084 DDMI2, n=90, r.m.s.e=0.584, R 2=0.93). The DDMI, which was representative of the nutrient input, was driven mainly by DMI rather than dry matter digestibility, whereas these two parameters did not correlate (r=0.068, P=0.56). The average global FCE (0.11 g ADG/g DDMI) showed a greater association with the metabolic FCE (0.17 g ADG/g DMI) than the digestive FCE (0.62). The CI (g DM/kg BW) increased ADG (ADG=2376+CI 56.1, n=16, r.m.s.e.=441, R 2=0.95). The SR expressed as kg BW/ha decreased the individual ADG by 1.19 g/kg BW per additional ton of BW/ha, whereas the global ADG calculated per ha increased by 0.57 per additional ton BW/ha. When the SR was expressed as kg BW/ton DM and per ha rather than as kg BW/ha, the impact on the individual ADG decreased by 0.18 or 0.86 g per additional ton BW/ha, depending on the initial BW of the cattle. These results provide a better view of the potential performance and feeding of cattle in tropical grasslands. The results provide an improved quantification of the relationships between diet and performance, as well as the overall quantitative impact of SR and supplementation.

New ways of measuring intake, efficiency and behaviour of grazing livestock

Wireless sensor networks (WSN) offer a novel method for measuring important livestock phenotypes in commercial grazing environments. This information can then be used to inform genetic parameter estimation and improve precision livestock management. Arguably, these technologies are well suited for such tasks due to their small, non-intrusive form, which does not constrain the animals from expressing the genetic drivers for traits of interest. There are many technical challenges to be met in developing WSN technologies that can function on animals in commercial grazing environments. This paper discusses the challenges of the software development required for the collection of data from multiple types of sensors, the management and analyses of the very large volumes of data, determination of which sensing modalities are sufficient and/or necessary, and the management of the constrained power source. Assuming such challenges can be met however, validation of the sensor accuracy against benchmark data for specific traits must be performed before such a sensor can be confidently adopted. To achieve this, a pasture intake research platform is being established to provide detailed estimates of pasture intake by individual animals through chemical markers and biomass disappearance, augmented with highly annotated video recordings of animal behaviours. This provides a benchmark against which any novel sensor can be validated, with a high degree of flexibility to allow experiments to be designed and conducted under continually differing environmental conditions. This paper also discusses issues underlying the need for new and novel phenotyping methods and in the establishment of the WSN and pasture intake research platforms to enable prediction of feed intake and feed efficiency of individual grazing animals.