Morphological Description and Live Weight Prediction from Body Measurements of Socorro Island Merino Lambs

The aim of this study was to describe the morphology and estimate live weight from body measurements of Socorro Island Merino lambs. A group of Socorro Island Merino lambs was recorded from birth to year for live weight, rump width, rump length, withers height, body length, cannon bone perimeter, and chest girth, width, and depth. The effect of the lamb type on body measurements and live weight was analyzed using ANOVA, Pearson's correlation analysis was performed to estimate the relationship between body measurements and live weight, multiple linear regressions were fitted to obtain prediction equations of live weight from the body measurements and finally, chest girth was used to generate prediction equations using linear and exponential models. At birth and at year, differences were observed in body measurements, especially those related to the thoracic region, with crossbred males showing the highest values. Live weight was correlated with almost all the body measurements, with the highest coefficients observed with chest girth, chest width, and chest depth. Live weight can be accurately predicted from multiple regression equations using several body measurements, but using only chest girth (CG) as a predictor, the exponential equation W_0-365 = 0.9142 exp^{(0.0462 CG)} showed the best accuracy.

Review: Precision Livestock Farming technologies in pasture-based livestock systems

Precision Livestock Farming (PLF) encompasses the combined application of single technologies or multiple tools in integrated systems for real-time and individual monitoring of livestock. In grazing systems, some PLF applications could substantially improve farmers' control of livestock by overcoming issues related to pasture utilisation and management, and animal monitoring and control. A focused literature review was carried out to identify technologies already applied or at an advanced stage of development for livestock management in pastures, specifically cattle, sheep, goats, pigs, poultry. Applications of PLF in pasture-based systems were examined for cattle, sheep, goats, pigs, and poultry. The earliest technology applied to livestock was the radio frequency identification tag, allowing the identification of individuals, but also for retrieving important information such as maternal pedigree. Walk-over-weigh platforms were used to record individual and flock weights. Coupled with automatic drafting systems, they were tested to divide the animals according to their needs. Few studies have dealt with remote body temperature assessment, although the use of thermography is spreading to monitor both intensively reared and wild animals. Global positioning system and accelerometers are among the most applied technologies, with several solutions available on the market. These tools are used for several purposes, such as animal location, theft prevention, assessment of activity budget, behaviour, and feed intake of grazing animals, as well as for reproduction monitoring (i.e., oestrus, calving, or lambing). Remote sensing by satellite images or unmanned aerial vehicles (UAVs) seems promising for biomass assessment and herd management based on pasture availability, and some attempts to use UAVs to monitor, track, or even muster animals have been reported recently. Virtual fencing is among the upcoming technologies aimed at grazing management. This system allows the management of animals at pasture without physical fences but relies on associative learning between audio cues and an electric shock delivered if the animal does not change direction after the acoustic warning. Regardless of the different technologies applied, some common constraints have been reported on the application of PLF in grazing systems, especially when compared with indoor or confined livestock systems. Battery lifespan, transmission range, service coverage, storage capacity, and economic affordability were the main factors. However, even if the awareness of the existence and the potential of these upcoming tools are still limited, farmers' and researchers' demands are increasing, and positive outcomes in terms of rangeland conservation, animal welfare, and labour optimisation are expected from the spread of PLF in grazing systems.

Effect of Breeding Heavier Romney Ewe Lambs at Seven Months of Age on Lamb Production and Efficiency over Their First Three Breeding Seasons

Simple Summary

This experiment examined the impact of breeding heavier ewe lambs on lamb production and the ability of a ewe to produce a high number and heavier lambs (i.e., efficiency) over the first three breeding seasons. Two groups of ewe lambs were bred at seven months of age at an average live weight of either 47.9 ± 0.36 kg (n = 135) or 44.9 ± 0.49 kg (n = 135). Ewe live weight, number of lambs born and weaned, and lamb live weights were recorded, and ewe efficiency was calculated for each ewe. The weight and number of lambs at weaning over the three-year period did not differ between treatments, however, when data were pooled, heavier ewe lambs at breeding had a greater number and lamb weaning weight over the three-year period. Breeding heavier ewe lambs had no effect on ewe efficiency. There was a positive relationship between ewe lamb breeding live weight and their mature weight. These results suggest that breeding heavier ewe lambs increased the total lamb production over the three-year period, however, it had no effect on ewe efficiency. Before final recommendations can be made, ewe lifetime performance and longevity of heavier ewe lambs at breeding are required.

Abstract

This experiment examined the effect of breeding heavier ewe lambs on lamb production and their efficiency over their first three breeding seasons. Two groups of ewe lambs were bred at seven months of age at an average pre-breeding live weight of either 47.9 ± 0.36 kg (heavy; n = 135) or 44.9 ± 0.49 kg (control; n = 135). Ewe live weight, number of lambs born and weaned, and lamb live weight were recorded until 39 months of age, and efficiency was calculated for each ewe. Although the number and lamb weaning weight did not differ between treatments over three years, when data were pooled, heavier ewe lambs at breeding weaned a greater number of lambs over the three-year period. The total lamb weaning weight over the three-year period increased by 2% for each additional kilogram at ewe lamb breeding. Breeding heavier ewe lambs had no effect on efficiency. These results suggest that although breeding heavier ewe lambs had a positive effect on lamb production over the three-year period, it had no effect on efficiency. Before final recommendations can be made, lifetime performance and longevity to five years of age of heavier ewe lambs at breeding are required.

Predicting ewe body condition score using adjusted liveweight for conceptus and fleece weight, height at withers, and previous body condition score record

The relationship between ewe body condition score (BCS) and liveweight (LW) has been exploited previously to predict the former from LW, LW-change, and previous BCS records. It was hypothesized that if fleece weight and conceptus-free liveweight and LW-change, and in addition, height at withers were used, the accuracy of current approaches to predicting BCS would be enhanced. Ewes born in 2017 (n = 429) were followed from 8 mo to approximately 42 mo of age in New Zealand. Individual ewe data were collected on LW and BCS at different stages of the annual production cycle (i.e., prebreeding, at pregnancy diagnosis, prelambing, and weaning). Additionally, individual lambing dates, ewe fleece weight, and height at withers data were collected. Linear regression models were fitted to predict current BCS at each ewe age and stage of the annual production cycle using two LW-based models, namely, unadjusted for conceptus weight and fleece weight (LW alone1) and adjusted (LW alone2) models. Furthermore, another two models based on a combination of LW, LW-change, previous BCS, and height at withers (combined models), namely, unadjusted (combined1) and adjusted for conceptus and fleece weight (combined2), were fitted. Combined models gave more accurate (with lower root mean square error: RMSE) BCS predictions than models based on LW records alone. However, applying adjusted models did not improve BCS prediction accuracy (or reduce RMSE) or improve model goodness of fit (R²) (P > 0.05). Furthermore, in all models, both LW-alone and combined models, a great proportion of variability in BCS, could not be accounted for (0.25 ≥ R² ≥ 0.83) and there was substantial prediction error (0.33 BCS ≥ RMSE ≥ 0.49 BCS) across age groups and stages of the annual production cycle and over time (years). Therefore, using additional ewe data which allowed for the correction of LW for fleece and conceptus weight and using height at withers as an additional predictor did not improve model accuracy. In fact, the findings suggest that adjusting LW data for conceptus and fleece weight offer no additional value to the BCS prediction models based on LW. Therefore, additional research to identify alternative methodologies to account for individual animal variability is still needed.

Lambs' live weight estimation using 3D images

The sheep sector has not suffered the technification that other livestock sectors have. The lack of technological knowledge of the farmers and the economic limitations of the sector have made this technification difficult. One of the most widely used technologies is Precision Livestock Farming (PLF). PLF has already been used in other livestock sectors to improve farming efficiency. In the light of the problem that sheep farmers have in weighing lambs and their low precision, this paper proposes a system for estimating weight by means of 3D image capture. Thus, zenithal images of 272 lambs have been recorded. They have been processed using the capture of the upper area and the average depth of the pixels of the lamb. This estimates the weight of the animal with an error of less than 6%. This technology has a low economic cost and is easy to operate, helping farmers to be more willing to use it. This method manages to reduce the duration of the process, the stress of the animal and to improve the overall accuracy in weight estimation. Thus, it will help to have a greater control of the weight of the animal and to improve the economic profitability that the farmer obtains for the lambs.

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.

Phenotypic factors associated with lamb live weight and carcass composition measurements in an Irish multi-breed sheep population

Understanding the phenotypic factors that affect lamb live weight and carcass composition is imperative to generating accurate genetic evaluations and further enables implementation of functional management strategies. This study investigated phenotypic factors affecting live weight across the growing season and traits associated with carcass composition in lambs from a multibreed sheep population. Four live weight traits and two carcass composition traits were considered for analysis namely; birth, preweaning, weaning, and postweaning weight, and ultrasound muscle depth and fat depth. A total of 427,927 records from 159,492 lambs collected from 775 flocks between the years 2016 and 2019, inclusive were available from the Irish national sheep database. Factors associated with live weight and carcass composition were determined using linear mixed models. The heaviest birth, preweaning, and weaning weights were associated with single born lambs (P < 0.001), however by postweaning, there was no difference observed in the weights of single and twin born lambs (P > 0.01). Breed class affected lamb live weight and carcass composition with terminal lambs weighing heaviest and having greater muscle depth than all other breed classes investigated (P < 0.001). Lambs born to first parity dams were consistently lighter, regardless of time of weighing (P < 0.001), while dams lambing for the first time as ewe lambs produced lighter lambs than those lambing for the first time as hoggets (P < 0.001). Greater heterosis coefficients (i.e., >90% and ≤100%) resulted in heavier lambs at weaning compared with lambs with lower levels of heterosis coefficients (P < 0.001). A heterosis coefficient class <10% resulted in lambs with greater muscle depth while recombination loss of <10% increased ultrasound fat depth (P < 0.001). Results from this study highlight the impact of multiple animal level factors on lamb live weight and carcass composition which will enable more accurate bio-economic models and genetic evaluations going forward.

Comparison of linear and nonlinear models to estimate body weight of Pelibuey ewes from body measurements

The aim of this study was to develop prediction equations for the body weight of Pelibuey ewes from body measurements comparing linear vs nonlinear models. A subsample of 197 ewes was scored for body weight (BW), rump length (RL), rump width (RW), height at withers (HW), chest girth (CG), chest width (CW), chest depth (CD), cannon bone perimeter (CP), and body length (BL). Pearson's correlation analysis was performed on a sub-data set from 197 ewes to estimate the relationship between body weight and body measurements. Multiple linear regressions were fitted to obtain prediction equations of body weight from the eight body measurements, and prediction equations were obtained from the body measurement that showed the highest correlation with body weight using five nonlinear models allometric, saturation growth, exponential, and incomplete gamma. Data from an independent subsample of 196 ewes was used to validate the equation with the best goodness of fit using linear regression analysis. CG was the body measurement that showed the highest correlation with BW, and based on multiple stepwise regression, in the equation BW = - 60.622 + 1.233CG explained 79% of the body weight variation. Moreover, BW prediction was more accurate when other measurements such CW, BL, and RW were added to the model generating to the equation BW = - 68.875 + 0.845CG + 0.866CW + 0.195BL + 0.601RW (R² = 0.85, MSE = 15.51). In the case of nonlinear models, incomplete gamma and exponential models generated the equations with the best goodness of fit and precision: BW = 0.077CG^1.108exp^(0.016CG) (R² = 0.82, MSE = 18.64) and BW = 3.5759exp^(0.0292CG) (R² = 0.82, MSE = 18.65) respectively.

Using a lamb's early-life liveweight as a predictor of carcass quality

The commercial value of lamb carcasses is primarily determined by their weight and quality, with the latter commonly quantified according to muscle coverage and fat depth. The ability to predict these quality scores early in the season could be of substantial value to sheep producers, as this would enable tailored flock management strategies for different groups of animals. Existing methods of carcass quality prediction, however, require either expensive equipment or information immediately before slaughter, leaving them unsuitable as a decision support tool for small to medium-scale enterprises. Using seven-year high-resolution data from the North Wyke Farm Platform, a system-scale grazing trial in Devon, UK, this paper investigates the feasibility of using a lamb's early-life liveweight to predict the carcass quality realised when the animal reaches the target weight. The results of multinomial regression models showed that lambs which were heavier at weaning, at 13 weeks of age, were significantly more likely to have leaner and more muscular carcasses. An economic analysis confirmed that these animals produced significantly more valuable carcasses at slaughter, even after accounting for seasonal variation in lamb price that often favours early finishers. As the majority of heavier-weaned lambs leave the flock before lighter-weaned lambs, an increase in the average weaning weight could also lead to greater pasture availability for ewes in the latter stage of the current season, and thus an enhanced ewe condition and fertility for the next season. All information combined, therefore, a stronger focus on ewes' nutrition before and during lactation was identified as a key to increase system-wide profitability.

Review: Precision livestock farming, automats and new technologies: possible applications in extensive dairy sheep farming

Precision livestock farming (PLF) technologies are becoming increasingly common in modern agriculture. They are frequently integrated with other new technologies in order to improve human-livestock interactions, productivity and economical sustainability of modern farms. New systems are constantly being developed for concentrated farming operations as well as for extensive and pasture-based farming systems. The development of technologies for grazing animals is of particular interest for the Mediterranean extensive sheep farming sector. Dairy sheep farming is a typical production system of the area linked to its historical and cultural traditions. The area provides roughly 40% of the world sheep milk, having 27% of the milk-producing ewes. Developed countries of the area (France, Italy, Greece and Spain - FIGS) have highly specialized production systems improved through animal selection, feeding techniques and intensification of production. However, extensive systems are still practiced alongside intensive ones due to their lower input costs and better resilience to market fluctuations. In the current article, we evaluate possible PLF systems and their suitability to be incorporated in extensive dairy sheep farming as practiced in the FIGS countries. Available products include: electronic identification systems (now mandatory in the EU) such as ear tags, ruminal boluses and sub-cutaneous radio-frequency identification; on-animal sensors such as accelerometers, global positioning systems and social activity loggers; and stationary management systems such as walk-over-weights, automatic drafter (AD), virtual fencing and milking parlour-related technologies. The systems were considered according to their suitability for the management and business model common in dairy sheep farming. However, adoption of new technologies does not take place immediately in small and medium scale extensive farming. As sheep farmers usually belong to more conservative technology consumers, characterized by an average age of 60 and a very transparent community, the dynamics do not favour financial risk taking involved with new technologies. Financial barriers linked to production volumes and resource management of extensive farming are also a barrier for innovation. However, future prospectives could increase the importance of technology and promote its wider adoption. Trends such as global sheep milk economics, global warming, awareness to animal welfare, antibiotics resistance and European agricultural policies could influence the farming practices and stimulate wider adoption of PLF systems in the near future.

Precision finishing of South African lambs in feedlots: a review

In the intensification of sheep production systems, feedlot finishing plays a fundamental role in preparing lambs for slaughter, as well as relieving the grazing pressure on pasture. The profit margins in feedlot operations are often narrow and require the economics of scale to generate a sufficient income. In order to minimise expenses, intensive management and precision rearing of lambs to an ideal slaughter weight is needed to obtain premium carcass prices. The South African sheep industry is made up of wool, dual-purpose as well as meat type breeds, which also vary in terms of maturity. In order to implement precision finishing of South African lamb, a complete understanding of the growth, intake and fat deposition trends of growing lambs of different breed types is needed. This review outlines feedlot lamb production within the Southern African context for the major commercial breeds, while also providing insight in the considerations necessary to develop a decision support system for lamb rearing. Integrating such a decision support system into a lamb feedlot operation can then be used for precision finishing of lambs by predicting the optimal length of the feeding period and ideal slaughter weights of lambs.

The Effect of Age, Stage of the Annual Production Cycle and Pregnancy-Rank on the Relationship between Liveweight and Body Condition Score in Extensively Managed Romney Ewes

Simple Summary

Liveweight and body condition score (BCS)) are related, indicating that it may be possible to predict one from the other. The magnitude of this relationship can be altered by animal and environmental factors. The aim of the present study was to determine the nature of the association between liveweight and BCS over time, and the effect of the interaction between the stage of the annual cycle and the age of the ewe, using individual animal records. The association between liveweight and BCS was found to be linear and was affected by the interaction between ewe age, stage of the annual cycle and pregnancy-rank of the ewe. The results highlight the substantial contribution of BCS to the differences in liveweight of the ewe. The findings suggest that when predicting BCS from the liveweight, consideration of these factors is required, and different prediction equations are needed.

Abstract

This study determined the nature of the relationship between liveweight and body condition score (BCS) and assessed the influence of the stage of the annual cycle and pregnancy-rank on the relationship between liveweight and BCS in Romney ewes. Data were collected from the same ewes at different ages (8–18, 19–30, 31–42, 43–54, 55–66 and ≥67 months), stages of the annual cycle (pre-breeding, at pregnancy diagnosis, pre-lambing and weaning) and pregnancy-rank (non-pregnant, single or twin). Linear regression was determined as being sufficient to accurately describe the relationship between liveweight and BCS. Across all data, a one-unit change in BCS was associated with 6.2 ± 0.05 kg liveweight, however, this differed by stage of the cycle, pregnancy-rank and ewe age (p <0.05). The average liveweight per unit change in body condition score increased with the age of the ewe and was greatest at weaning and lowest pre-lambing. Among pregnancy-ranks, the average liveweight per unit change was also greater during pregnancy diagnosis than pre-lambing and was greatest among single and lowest in non-pregnant ewes. The results support the hypothesis that the relationship between liveweight and BCS is affected by the interaction between stage of the annual cycle, pregnancy-rank and ewe age.

Reproductive performance in goats and causes of perinatal mortality: a review

Goat meat production is an expanding industry in Australia. However, there is limited data quantifying the levels of reproductive performance, particularly under extensively grazed rangeland conditions, which would inform interventions to improve performance. This review aimed to quantify the levels of reproduction, time and causes of reproductive wastage in goats. It considers the levels of fertility, fecundity, embryonic loss, fetal loss and post-natal survival reported under Australian conditions, and comparisons are made with international reports. Key management factors that may contribute to reproductive performance include breed, seasonality, nutritional conditions, and weather conditions at kidding. While goats are potentially prolific breeders, in Australia, the variation in weaning rate (kids/doe joined) among properties is large (51–165%), although the causes of this variation are not well defined. Generally, conception and kidding rates are high, although fetal loss associated with undernutrition is more likely in goats than sheep. As with sheep, perinatal losses are generally the largest source of wastage, with an average 20% kid mortality, but this level is influenced by litter size and appears to be higher under extensive rangeland systems. The causes of perinatal kid loss under Australian conditions are similar to those in sheep, with starvation–mismothering–exposure and dystocia or stillbirth the key causes. Studies are needed to accurately quantify the level and causes of reproductive wastage in commercial herds, including a range of management situations, to enable effective interventions to be developed.

Intra-flock variability in the body reserve dynamics of meat sheep by analyzing BW and body condition score variations over multiple production cycles

Breeding for resilience requires a better understanding of intra-flock variability and the related mechanisms responsible for robustness traits. Among such traits, the animals' ability to cope with feed fluctuations by mobilizing or restoring body reserves (BR) is a key mechanism in ruminants. The objective of this work was to characterize individual variability in BR dynamics in productive Romane ewes reared in extensive conditions. The BR dynamics profiles were characterized by combining individual longitudinal measurements of BW and body condition scores (BCS) over several production cycles. Historical data, including up to 2628 records per trait distributed in 1146 ewes, underwent cluster analysis. Two to four trajectories were observed for BW depending on the cycle, while three trajectories were found for BCS, whatever the cycle. Most trajectories suggested that BR dynamics were similar but the level of BR may differ between ewes. Nevertheless, some trajectories suggested that both BR dynamics and levels were different for a proportion of ewes. Clustering on BW and BCS profiles adjusted for individual level trends, resulted in differences only in the level of BW or BCS, rather than differences in trajectories. Thus, the overall shape of trajectories was not changed considering or not the individual level trend across cycles. In addition to individual variability, the ewe's age at first lambing and litter size contributed to the distribution of the ewes between the trajectories. Regarding the entire productive life, three trajectories were observed for BW and BCS changes over three productive cycles. Increase in BW at each cycle suggested that ewes kept growing up until 3 to 4 years old in our conditions. Similar alternation of BCS gains and losses across cycles suggested BR dynamics might be repeatable. Many individual trajectories remained the same throughout a ewe's life, whatever the age at first lambing, parity or litter size. Our results demonstrate the relevance of using BW and BCS changes for characterizing the diversity of BR mobilization-accretion profiles in sheep in a long timespan perspective.

Genetic analysis of robustness in meat sheep through body weight and body condition score changes over time

Animal robustness may be defined as a complex trait characterizing the ability of an individual to be adapted, productive and healthy under contrasted and fluctuating environmental situations. Such a trait is now considered an essential criterion in order to meet the more ambitious goals of farming sustainability. In ruminants, one of the key mechanisms via which robustness is expressed is the capacity to mobilize or restore body reserves (BR) to cope with the challenges of negative energy balances. The objectives of this work were as follows: 1) to estimate the genetic parameters related to BR dynamics in ewes over successive production cycles and 2) to investigate BR management relationships between different physiological stages. For this, historical individual BW and BCS data from 2,920 phenotyped ewes were used for genetic analysis. The changes in BW (∆BW) and BCS (∆BCS) over time were analyzed. Eight physiological stages were considered to investigate these changes over time: mating, early pregnancy, mid-pregnancy, lambing, early suckling, mid-suckling, weaning, and postweaning. The estimated heritability were low for both ∆BW (h2 = 0.13 to 0.18) and ∆BCS (h2 = 0.04 to 0.16). Moderate to high positive genetic correlations (from 0.48 to 0.91) were obtained between BR mobilization phases and between BR accretion phases. Similarly, moderate to high negative genetic correlations (from -0.36 to -0.75) were estimated between the BR mobilization and accretion periods, suggesting that mechanisms driving BR mobilization and accretion processes were genetically correlated. This is the first study in ruminants that demonstrate that the extent and temporal changes in profiles of BR mobilization and accretion are heritable and genetically linked, indicating that such traits could be considered in genetic programs aimed at improving robustness. Nevertheless, further research is needed for a more comprehensive understanding of BR dynamics, notably by including other physiological parameters (i.e., metabolites and hormones) and additional information on the productive and reproductive life of the ewe.

ASKBILL as a web-based program to enhance sheep well-being and productivity

ASKBILL is a web-based program that uses farm measurements, climate data and information on genetics to predict pasture growth, animal performance and animal health and climate risks. The program uses several biophysical models, which are customised by user inputs, localised daily weather updates and a dynamical probabilistic 90-day climate forecast to enhance sheep well-being and productivity. This approach can minimise the requirement for manual, auto and remote measurements, thus reducing labour requirements and complexity. In this article, the animal growth model provides an example of a biophysical model used to provide predictions. This is an energy-based model and the model parameterisation is designed to be physiologically meaningful and able to be customised for the genetic merit of the animal using a growth coefficient that calibrates growth of body components and energy requirements. A key feature of the animal growth model is its forecast projections, which are based on an ensemble of simulations. The model can estimate supplementary feeding rates required to achieve target liveweights and body condition scores and stocking rates required to achieve target pasture levels. The model can be customised for a farm and its livestock and is updated daily in response to climate data. This dynamic feature enables it to provide early stage alerts to users when animal production targets are unlikely to be met.