Application of In-Paddock Technologies to Monitor Individual Self-Fed Supplement Intake and Liveweight in Beef Cattle

Predicting Carcass Weight of Grass-Fed Beef Cattle before Slaughter Using Statistical Modelling

Gaining insights into the utilization of farm-level data for decision-making within the beef industry is vital for improving production and profitability. In this study, we present a statistical model to predict the carcass weight (CW) of grass-fed beef cattle at different stages before slaughter using historical cattle data. Models were developed using two approaches: boosted regression trees and multiple linear regression. A sample of 2995 grass-fed beef cattle from 3 major properties in Northern Australia was used in the modeling. Four timespans prior to the slaughter, i.e., 1 month, 3 months, 9-10 months, and at weaning, were considered in the predictive modelling. Seven predictors, i.e., weaning weight, weight gain since weaning to each stage before slaughter, time since weaning to each stage before slaughter, breed, sex, weaning season (wet and dry), and property, were used as the potential predictors of the CW. To assess the predictive performance in each scenario, a test set which was not used to train the models was utilized. The results showed that the CW of the cattle was strongly associated with the animal's body weight at each stage before slaughter. The results showed that the CW can be predicted with a mean absolute percentage error (MAPE) of 4% (~12-16 kg) at three months before slaughter. The predictive error increased gradually when moving away from the slaughter date, e.g., the prediction error at weaning was ~8% (~20-25 kg). The overall predictive performances of the two statistical approaches was approximately similar, and neither of the models substantially outperformed each other. Predicting the CW in advance of slaughter may allow farmers to adequately prepare for forthcoming needs at the farm level, such as changing husbandry practices, control inventory, and estimate price return, thus allowing them to maximize the profitability of the industry.

The time elapsed between assessments of blood metabolome and live weight affects associations between the abundance of metabolites and growth rate in beef cattle

INTRODUCTION

This study aimed to assess the associations between the relative abundance (RA) of blood metabolites and growth rate (i.e., live weight change, LWC) calculated using different intervals of time between live weight (LW) measurements from the metabolome assessment.

METHODS

Grazing beef cattle were raised for 56 days and blood samples from each animal were taken on day 57. Live weight was continuously measured using an automatic in-paddock weighing scale. The RA of plasma metabolites were determined using proton nuclear magnetic resonance (NMR). Live weight data were filtered for outliers and one LW record was selected every 1, 7, 14, 21, 28, 35, 42, 49 and 56 days before the metabolome assessment (LWC1 to LWC56, respectively). Live weight change was then re-calculated for each interval between LW data selected.

RESULTS

Associations between LWC calculations and the RA of metabolites were greatly affected by the interval of time between LW data selected. Thus, the number of significant associations decreased from 9 for LWC1 to 5 for LWC35 whereas no significant associations were found for LWC56 (P > 0.05). There were 7 metabolites negatively associated with LWC1 including leucine, 2-hydroxybutyrate, valine, creatinine, creatine, phenylalanine and methylhistidine; however, correlations were positive for 2 lipids. The strength of the correlation coefficients decreased as the length of the interval between LW measures increased although this reduction was greater for some metabolites such as leucine compared to others such as lipids. Our findings suggest that the time frame in which a particular response variable, such as LWC, is measured and metabolomic samples are taken could largely impact associations and thus conclusions drawn.

CONCLUSIONS

Depending on the variable to be explored, rapid changes in cattle metabolome may not be reflected in correlations if they are not assessed close in time. Our findings suggest that LWC should be measured for a period shorter than 28 days before the metabolome assessment as the number of significant associations decreases when LWC is measured for longer periods.

Provision of High-Quality Molasses Blocks to Improve Productivity and Address Greenhouse Gas Emissions from Smallholder Cattle and Buffalo: Studies from Lao PDR

Large ruminant production in developing countries is inefficient with low growth rates and likely high greenhouse gas emissions per unit of meat or milk produced. Trials conducted in Lao PDR from 2017 to 2020, studied ad libitum supplementation for 12 weeks with 20 kg high-quality molasses nutrient blocks (Four Seasons Pty Ltd., Brisbane, Australia), that were either non-medicated; fenbendazole-medicated (Panacur100®, Coopers Australia, 5 g/kg); triclabendazole-medicated (Fasinex®, Novartis Australia, 5 g/kg or 10 g/kg, respectively); or formulated with urea (8% or 10% urea, respectively). Average daily gains were determined for access to all molasses blocks and compared with access to control blocks, no supplementation, or previously determined free-grazing baseline average daily gains (55−84 g in cattle; 92−106 g in buffalo). Productivity was significantly improved following access to all molasses blocks. Average daily gains following access to 8% urea and control blocks were calculated for three age cohorts of cattle: young calves <8 m (238−298 g), growing cattle (143−214 g) and lactating cows (179−191 g). Modelling using IPCC Inventory software model V 2.69 of published data demonstrated a conservative net abatement of 350 kg CO2e was achievable over a 200-day feeding period. An additional trial of Emissions control blocks (n = 200) distributed to farmers (n = 60) and two educational institutions were conducted. Consumption rates (156 g/day) and farmer and institutional acceptance of these blocks were similar to our published findings with other molasses blocks, confirming all formulations of blocks improved animal productivity and body condition score, with healthier animals that were easier to manage. Modelling of changes in greenhouse gas emissions intensity identified an abatement of 470 kg CO2e per Emissions control blocks consumed, delivering a total project emissions abatement of 94 t CO2e. Provision of high-quality molasses blocks significantly improved smallholder large ruminant productivity and addition of greenhouse gas reducing agents is likely to achieve impressive abatement of greenhouse gas emissions due to improved efficiency of rumen fermentation and productivity.

Ruminant Lick Blocks, Particularly in China: A Review

A lick block (LB) is a solidified mixture of molasses, urea, minerals, filler, coagulant and binder that is supplemented to livestock mainly in relatively extensive rearing systems. It provides nutrients, such as soluble sugars, proteins, minerals and vitamins to balance dietary intake and can improve rumen fermentation and facilitate digestion and absorption of nutrients. These supplements improve livestock production, reproduction and carcass quality. In addition, LB can partially replace concentrate, serve as a delivery vehicle for additives such as enzymes and drugs and mediate the distribution of grazing livestock. This paper classifies and analyzes representative research; discusses the types, ingredients and current status of the utilization of LB; and systematically reviews the processing technology, quality assessment, influencing factors of intake, action mechanism and application. This review can provide a basis for the development, popularization and application of novel LB products.

The metabolomics profile of growth rate in grazing beef cattle

This study aimed to determine the relationship between the metabolome and changes in growth rate (i.e., liveweight change, LWC) and molasses-lick block supplement intake (MLB) of grazing cattle. Weaner beef cattle were fed for 220 days with a sequence of feed types and blood samples, growth rate, and supplement intake were taken on five points in time. The relative abundance (RA) of plasma metabolites were determined using proton nuclear magnetic resonance (NMR). Sixty-four per cent of the metabolites identified were associated with LWC but only 26% with MLB intake (P < 0.05). Periods with faster growth rate showed high availability of amino acids (i.e., valine, leucine, isoleucine, phenylalanine and tyrosine), acetate, and 3-hydroxybutyrate. Periods with lower growth rate were associated with high RA of lipids, choline and acetate. The metabolic profile of individual animals during a period of compensatory growth (after periods of poor performance) showed that high-performing animals were characterised by lower RA of amino acids (i.e., valine, leucine, isoleucine, methylhistidine), creatinine, creatine, pyruvate, 3-hydroxybutyrate, and acetyl groups. It is speculated that high-performing animals have faster uptake of these metabolites from the bloodstream. Cattle growth rate over time was associated with their metabolome which could be used to ensure that the availability of certain metabolites promoting growth is tailored in feed supplements to improve production.

Calf Birth Weight Predicted Remotely Using Automated in-Paddock Weighing Technology

The present study aimed to develop predictive models of calf birth weight (CBW) from liveweight (LW) data collected remotely and individually using an automated in-paddock walk-over-weighing scale (WOW). Twenty-eight multiparous Charolais cows were mated with two Brahman bulls. The WOW was installed at the only watering point to capture LW over five months. Calf birth date and weight were manually recorded, and the liveweight change experienced by a dam at calving (ΔLWC) was calculated as pre-LW minus post-LW calving. Cow non-foetal weight loss at calving (NFW) was calculated as ΔLWC minus CBW. Pearson's correlational analysis and simple linear regressions were used to identify associations between all variables measured. No correlations were found between ΔLWC and pre-LW (p = 0.52), or post-LW (p = 0.14). However, positive associations were observed between ΔLWC and CBW (p < 0.001, R2 = 0.56) and NFW (p < 0.001, R2 = 0.90). Thus, the results suggest that 56% of the variation in ΔLWC is attributed to the calf weight, and consequently could be used as an indicator of CBW. Remote, in-paddock weighing systems have the potential to provide timely and accurate LW data of breeding cows to improve calving management and productivity.

A Pilot Study Using Accelerometers to Characterise the Licking Behaviour of Penned Cattle at a Mineral Block Supplement

Simple Summary

Quantifying mineral block supplement intake by individual beef cattle is a challenging task but may enable improved efficiency of supplement use particularly in a grazed system. Estimating time spent licking when cattle access the mineral block supplement can be useful for predicting intake on an individual basis. The advancement of sensor technology has facilitated collection of individual data associated with ingestive behaviours such as feeding and licking duration. This experiment was intended to investigate the effectiveness of wearable tri-axial accelerometers fitted on both neck-collar and ear-tag to identify the licking behaviour of beef cattle by distinguishing it from eating, standing and lying behaviours. The capability of tri-axial accelerometers to classify licking behaviour in beef cattle revealed in this study would offer the possibility of measuring time spent licking and further developing a practical method of estimating mineral block supplement intake by individual grazing cattle.

Abstract

Identifying the licking behaviour in beef cattle may provide a means to measure time spent licking for estimating individual block supplement intake. This study aimed to determine the effectiveness of tri-axial accelerometers deployed in a neck-collar and an ear-tag, to characterise the licking behaviour of beef cattle in individual pens. Four, 2-year-old Angus steers weighing 368 ± 9.3 kg (mean ± SD) were used in a 14-day study. Four machine learning (ML) algorithms (decision trees [DT], random forest [RF], support vector machine [SVM] and k-nearest neighbour [kNN]) were employed to develop behaviour classification models using three different ethograms: (1) licking vs. eating vs. standing vs. lying; (2) licking vs. eating vs. inactive; and (3) licking vs. non-licking. Activities were video-recorded from 1000 to 1600 h daily when access to supplement was provided. The RF algorithm exhibited a superior performance in all ethograms across the two deployment modes with an overall accuracy ranging from 88% to 98%. The neck-collar accelerometers had a better performance than the ear-tag accelerometers across all ethograms with sensitivity and positive predictive value (PPV) ranging from 95% to 99% and 91% to 96%, respectively. Overall, the tri-axial accelerometer was capable of identifying licking behaviour of beef cattle in a controlled environment. Further research is required to test the model under actual grazing conditions.

Automatic Supplement Weighing Units for Monitoring the Time of Accessing Mineral Block Supplements by Rangeland Cattle in Northern Queensland, Australia

Time spent feeding by grazing cattle is an important predictor of intake and feed efficiency. This study examined the use of automatic supplement weighing (ASW) units for monitoring voluntary access of breeding cows (n = 430) to mineral block supplements in an extensive rangeland of northern Australia. The ASW units (n = 10) were located within each of experimental sites (5 units per site; Bore and Eldons). Over the 62 days of data collection, 85%, 13%, and 2% of cows spent <600, 600–1200, >1200 min accessing supplements, respectively, with between-animal variation (CV) of 107%. A total of 133 cows visited both sites while 142 and 155 cows visited only Bore and Eldons, respectively. Most visits (80–90%) were recorded during the day (800–1700 h), 7–17% during the night (1800–2300 h), and 3% during the dawn (0–700 h). Time spent accessing supplements differed between ASW units across the two sites (p < 0.001) and varied according to the day of visits (p < 0.001). There was a significant relationship between time spent at the ASW units and supplement intake on a herd basis (p < 0.001; R2adj = 0.70). The results showed that the ASW units were capable of monitoring access to mineral block supplements that may reflect the supplement intake of rangeland cattle.

Measuring liveweight changes in lactating dairy ewes with an automated walk-over-weighing system

Monitoring liveweight (LW) is an important part of sound management practices at the individual and flock level (e.g., controlling for nutritional status based on body condition, reproduction, and health-related issues), but it is time consuming and stressful. To our knowledge, no literature has reported on the evaluation of automated weighing systems in dairy sheep as an alternative to conventional static scales. The objective of this research was to evaluate the practical feasibility of using an automated walk-over-weighing (WoW) prototype to measure daily LW changes in dairy ewes without human intervention. We used adult Lacaune dairy ewes in 2 complementary trials conducted indoors. Trial 1 aimed at evaluating the repeatability, precision, and accuracy of LW measures recorded using WoW scales compared with a static scale (the gold standard). Forty-two adult ewes (LW ± standard deviation = 71.3 ± 10.4 kg) were randomly drafted from the main flock and used in a 1-day session. The trial included 3 passages. In each passage, ewes were weighed first on a static scale; once a static position was achieved and LW recorded, they continued the circuit and immediately traversed the WoW scale for an automated LW record. Trial 2 aimed to demonstrate the feasibility of using the WoW device under real-world conditions in a dairy sheep-farming system. The WoW scale was installed in the exit race of the milking parlor and evaluated over 7 wk with adult ewes in mid lactation (n = 93; LW 78.5 ± 8.1 kg). Once the ewes were acclimated to the WoW system, 1 group of ewes (n = 48) continued to receive the same feeding regimen (controls), and the other group (n = 45) underwent a nutritional challenge [challenged; 2 wk of undernutrition and then back to control regimen (refeeding) for 1 wk]. We evaluated the ability of the WoW to detect small changes in LW. We collected LW data (2 weighings per ewe per day) from the WoW after each of the 2 milking sessions (morning and evening). We also obtained LW values by weighing the ewes using a static scale once a week. The automated WoW system showed substantial agreement with the gold standard when assessed using Lin's concordance correlation coefficient and Bland and Altman's method, largely due to high repeatability. The WoW system was adequate for detecting small daily variations in LW during undernutrition and refeeding periods. Misbehaviors resulted in spurious WoW values in trial 2, requiring us to use filtration methods to exclude outlier weights and allow meaningful assessment of small LW changes. The WoW system evaluated here is an alternative to the static scales conventionally used on dairy sheep farms. If sound filtration of raw data is applied, WoW could contribute to the close (daily) monitoring of individual LW without operator intervention (i.e., voluntary weighing) and taking animal welfare into account (i.e., no stress related to the weighing session on static scales).

Advancements in sensor technology and decision support intelligent tools to assist smart livestock farming

Remote monitoring, modern data collection through sensors, rapid data transfer, and vast data storage through the Internet of Things (IoT) have advanced precision livestock farming (PLF) in the last 20 yr. PLF is relevant to many fields of livestock production, including aerial- and satellite-based measurement of pasture’s forage quantity and quality; body weight and composition and physiological assessments; on-animal devices to monitor location, activity, and behaviors in grazing and foraging environments; early detection of lameness and other diseases; milk yield and composition; reproductive measurements and calving diseases; and feed intake and greenhouse gas emissions, to name just a few. There are many possibilities to improve animal production through PLF, but the combination of PLF and computer modeling is necessary to facilitate on-farm applicability. Concept- or knowledge-driven (mechanistic) models are established on scientific knowledge, and they are based on the conceptualization of hypotheses about variable interrelationships. Artificial intelligence (AI), on the other hand, is a data-driven approach that can manipulate and represent the big data accumulated by sensors and IoT. Still, it cannot explicitly explain the underlying assumptions of the intrinsic relationships in the data core because it lacks the wisdom that confers understanding and principles. The lack of wisdom in AI is because everything revolves around numbers. The associations among the numbers are obtained through the “automatized” learning process of mathematical correlations and covariances, not through “human causation” and abstract conceptualization of physiological or production principles. AI starts with comparative analogies to establish concepts and provides memory for future comparisons. Then, the learning process evolves from seeking wisdom through the systematic use of reasoning. AI is a relatively novel concept in many science fields. It may well be “the missing link” to expedite the transition of the traditional maximizing output mentality to a more mindful purpose of optimizing production efficiency while alleviating resource allocation for production. The integration between concept- and data-driven modeling through parallel hybridization of mechanistic and AI models will yield a hybrid intelligent mechanistic model that, along with data collection through PLF, is paramount to transcend the current status of livestock production in achieving sustainability.