Animal - Open Space最新文献

A considerable opportunity exists in evaluating the dynamics of the carcass and the processing cut-up weights of broilers across the whole grow-out period as influenced by intervention factors. However, no fast and objective tool exists up to date to make such determinations. This study aimed to develop models to predict the unchilled and chilled weights of the carcass and cut-up pieces of broilers using Dual-Energy X-ray Absorptiometry (DEXA) and feathered non-fasted birds. Highly diverse (BW and body composition) broilers (n = 291) between 4 and 79 days of age were euthanized, DEXA-scanned, and manually processed to determine the weights of the carcass and cut-up pieces. Correction factors were applied to obtain the fasted BW and the corresponding bled and chilled weights. A database was built up, including all the weights recorded and the DEXA-reported indexes. A stratified random data-splitting with a refitting approach was applied. Multiple least-squares linear regressions were fitted for each unchilled and chilled variable on the training dataset using JMP Pro 16. Natural log and square root transformations were applied to predictor variables as convenient, and outliers were removed. Candidate models were screened for normal distribution and homoscedasticity of residuals and collinearity among predictors. The highest precision (adjusted R²) and the lowest error (RMSE) were selection criteria. Once model overfitting and prediction performance was tested on the validation dataset, the models were refitted with all the data in the original dataset. Prediction models with high (unchilled and chilled carcass and cut-up weights, feet, and head; R² > 0.99) and acceptable (abdominal fat; R² > 0.69) precision were obtained. In conclusion, these results support the use of DEXA to determine the processing weights of broilers. Its application to the study of growth curves of cut-up pieces as influenced by nutrition, genetics, environment, and management opens a new spectrum of opportunities for the industry.

We collected data on the behaviour of dairy cows in barns, clinical signs of diseases as well as events that may stress or agitate the cows. A Real-Time Locating System gives the position of individual cows every second. The position of the cow is determined by triangulation based on radio waves emitted by a tag fixed on each cow neck collar and captured by antennas in the barn. The cow’s activity is inferred from its position: ‘eating’ if the cow is positioned at the feeding table, ‘resting’ if the cow is in a resting area (typically cubicles), else ‘in alleys’. We aggregated this information to get the time spent in each activity per hour. We also calculated the activity level of the cow for each hour of the day by attributing a weight to the time spent in each activity. For each cow and day, we collected information on health events or other events that may affect behaviour. There were 11 types of events. Six events were linked to health: lameness; mastitis; LPS (i.e. administration of lipopolysaccharide (LPS) in the mammary gland, an experimental treatment to induce udder inflammation); subacute ruminal acidosis; other diseases (such as colic, diarrhoea, ketosis, milk fever or other infectious diseases); and accidents (such as retained placenta or vaginal laceration). Two events were linked to reproduction: oestrus and calving. Three events were stress events: animal mixing, disturbance (i.e. mild intervention on animals such as late feeding, alarm test) and marginal management changes (ration changes, fill bed). In addition, a Boolean sums up whether this hour was considered as normal or not. Data contain four datasets. It consists of univariate time series. Each time series corresponds to the hourly activity level of a cow. Datasets 1 and 2 are from the INRAE Herbipôle experimental farm and include data from experiments; datasets 3 and 4 are from commercial farms. They contain data on respectively 28, 28, 30 and 300 cows monitored for 6 months, 2 months, 40 days and one year. The data can be used to study the links between health, reproduction events and stress on the one hand and cow behaviour on the other hand. More specifically, it can be used to build and test tools for an earlier detection of health and disturbances, with a view to inform caretakers so that corrective actions can be rapidly put in place.

In the present case study, the welfare of local breed (Krškopolje pig) pigs reared indoors, outdoors, and in combined production systems (total of 10 farms) were evaluated. The multidimensional Welfare Quality® assessment protocol, with slight modifications, was used to evaluate the farms. Animal-based observations were used to examine the four main principles of welfare (good feeding, good housing, good health, and appropriate behaviour) and their twelve independent welfare sub-criteria. Scores for each criterion were calculated and each farm was classified into one of the four welfare categories (excellent, enhanced, acceptable, or not classified). Maximal total scores were determined for the “good feeding” principle in the indoor and combined systems, whereas the outdoor system had a lower score (64) because of insufficient water troughs. In the case of “good housing” principle, maximal total scores were attributed to outdoor system, and lower scores for indoor and combined systems (72 and 84, respectively) due to the lesser space allowance, dirtiness and shivering in pigs. The scores for the “principle of good health” were rather low in all husbandry systems (62, 58, 61 for outdoor, indoor and combined systems, respectively), mainly because of the castration method practised without pain relief medication. The integrated score for “appropriate behaviour” was lower in indoor systems, because of lower “exploratory behaviour” scores (64). Considering the overall assessment across all principles, farms having only outdoor or only indoor system were classified as “enhanced” (total score of 77 and 74, respectively), whereas farms with combined system (indoor housing with outdoor access) were classified as “excellent” (total score of 85). The present study showed high welfare quality of housing systems with local breed Krškopolje pigs.

Among the available approaches to study the bioavailability of nutrients, both the body deposition and the intestinal balance methods have technical and analytical particularities that make them inconsistent to study the bioavailability of trace elements. This study describes an approach that allows assessing the net postintestinal bioavailability of trace elements. This approach is based on the real-time fluxes of nutrients flowing in the portal vein. In contrast with other methods, this technique allows monitoring the real-time postmeal profile of net fluxes of nutrients in order to compare multiple types of meal treatments within the same animal.

Respiration chambers are the gold standard technique for measuring methane in ruminants provided that their gas recovery rates are close to 100%. The determination of the gas recovery rate of respiration chamber facilities is a central prerequisite to assess the accuracy of the methane emission quantification. However, data of recovery tests are seldom reported. This paper presents data from gas recovery tests applied to an experimental facility of four open-circuit respiration chambers designed to measure methane emissions from goats. The experimental facility is located at Thiverval-Grignon, France. The recovery test was assessed by placing a known source of methane emission at six locations in each chamber successively. For each chamber, the gas from the chamber and the ambient air were continuously sampled by a Multi-Gas Analyser 3500 gas analyser provided with a multiport unit that switches the sampling between the pipe from chamber and from the ambient air every 90 s. The analyser determines the concentration (ppm) of methane by infrared. The data were further imported in an R script for calculation of the methane recovery percentage. These data are useful resources for illustrating the protocol to assess the accuracy of respiration chambers.

This paper investigates how pig housing relates to diversity and circularity of farms and how this influences the capacity of European organic pig producers to cope with economic, legislation, labour and climate-related shocks. It identifies resilience strategies of pig producers in Europe by analysing resilience capacity and attributes to different shocks, namely input and output price shocks, disease outbreaks, climate change, legislation change and labour fluctuations. Based on narratives of 18 pig producers, this paper finds three resilience strategies: an efficiency-based strategy, a nutrient substitution strategy and a farm diversification strategy. Non-resiliency is mostly found among the producers with an all-year outdoor production system following the nutrient substitution strategy related to low feed self-sufficiency. The producers follow an efficiency-based strategy when they cannot accumulate reserves sufficient to cope with shocks. Non-resilience among the farm diversification strategy is related to direct marketing that is labour intensive requires the ability to pay decent wages. To increase the resilience of pig producers in Europe, policies should recognise that these different strategies exist and tailor policies differently for different types of producers.

Coupling genetic and mechanistic models is appealing to explore the impact of energy trade-offs on the expression of feed efficiency traits in dairy cattle and predict selection response. The objective of this study was to evaluate the sensitivity of genetic (co)variances among milk production and feed efficiency (FE) traits simulated with a mechanistic dairy cow model depending on the genetic variability assumed for input parameters. The cow model was calibrated for a grass-based production and included a genetic module. Four genetically driven input parameters described the energy acquisition and allocation to different biological functions of cows. In each simulation, a population of 20 000 cows from 200 unrelated sires was simulated. The nutritional environment was an input of the model and was tailored by modulating feed offer and quality. A non-limiting nutritional environment was simulated to mimic a situation of ad libitum feeding and was used as a reference. Two other scenarios were simulated by imposing a moderate and a high DM intake restriction on simulated cows. Five phenotypes related to milk production and FE were considered: milk production, BW at calving, DM intake, lactation efficiency and body reserves during early lactation. These traits were estimated both in first and third lactations. A baseline scenario was defined considering a heritability of 0.35 and a phenotypic CV of 10% for acquisition and allocation parameters (AAPs). Different scenarios were explored by reducing the heritability to 0.15 or increasing CV to 20 and 30% or both. Heritabilities and genetic correlations between simulated traits were estimated using animal linear mixed models. Each scenario was replicated 20 times. Simulated performance and genetic parameters for these traits were compared across scenarios using an ANOVA. The heritability of AAPs only influenced the heritability of simulated traits. The phenotypic CV of AAPs mainly influenced the variability of simulated traits. However, increasing the CV also affected the number of cows reaching first and third lactation, due to the early culling of females with extreme AAPs profiles. Compared to other input parameters, the nutritional environment had the largest effect on both performance and genetic correlations between traits. Using a heritability value of 0.35 and a CV of 10% for all four AAPs enabled the simulation of milk production and FE performance with a realistic mean, variance and genetic correlations among traits in the three considered environments.

This work was originated from the need to study how animals individually react to environmental challenges. Common practical constraints in research protocols often lead to data collected at frequencies that are not high enough to capture the dynamics of animal responses. One approach to deal with that issue is to transform discrete empirical time series into continuous functions from which several descriptors can be extracted to characterise the response. A method for the extraction of smoothed functions from milk yield (MY) time series has been published before for dairy cows. This method was applied to detect challenges a posteriori. In this paper, we present an adaptation of this differential smoothing methodology, for the case when the environmental challenge is known a priori. This is advantageous because it allows for a more detailed characterisation of the response. Full description of the methodology is presented, where operations from differential calculus are applied to the smoothed functions to extract 23 descriptors that characterise the shape, dynamics and delay of individual responses to a single known challenge. We present examples of the application of the algorithm to individual time series of MY and plasma non-esterified fatty acid concentrations from suckling cows exposed to nutritional challenges that are known a priori. We propose a selection strategy for the smoothing coefficient (λ) based on the optimisation between noise reduction and output stability. If applied to groups of individuals that are sufficiently large, this methodology could provide information to help discriminating animals based on how they respond to the environmental challenges. This methodology may be used to develop decision-making tools for the selection of resilient individuals aiming at improving robustness and performance.

Heat stress is a growing problem in dairy cows, and interest is developing in calculating heat stress risk (Temperature Humidity Index – THI) without using specific farm data and in forecasting THI changes a few days in advance. Previous workers have shown that calculating THI values inside cattle sheds using data from local Meteorological Stations is not sufficiently accurate. Weather forecasting is becoming more local and can forecast on-farm temperature and humidity. This work looked at how well THI inside a cow shed could be predicted from data collected outside the cow shed on British farms. Six farms were monitored from 1 May 2021 to 30 Sept 2021 using bespoke data monitors that uploaded the data to the cloud in real time through the cellular network. Calculated THI values for inside and outside the cow shed were highly correlated (P < 0.001), and a regression predicting THI inside the shed from the THI outside the shed was highly significant (P < 0.001). However, farm-specific regressions had significantly different regression intercepts. Including calving pattern type (autumn or all year round) and calendar month separately or together improved the regression. The 95% confidence interval (CI) of the prediction was 10.8 THI units for the simple one-component model (THI_outside) and 7.8 for the three-component model (THI_outside, calendar month, calving pattern type). Farm-specific regressions had the lowest CI values suggesting there are farm-specific factors affecting THI that had not been captured. As a predictive model, the simple single component regression would be the most applicable but the relatively high CI means that predictions will not be that accurate with the risk of heat stress either under- or overemphasised on different farms. With one THI unit equating to approximately a 200 ml drop in milk yield in heat-stressed cows, such errors will be of biological and commercial significance. This in part may be due to the THI equation only considering temperature and humidity and ignoring solar radiation, shade, wind and animal factors such as milk yield, stage of pregnancy, weight and genetic variability. Further work is underway to develop an index that quantifies how the cow is responding to the combined heat-loading factors which may improve the prediction of heat stress.

Dual-Energy X-ray Absorptiometry (DEXA) has been shown to predict the processing weights of the carcass and cut-up pieces of the chicken. This study aimed to validate models to predict processing weights using DEXA that were previously developed. An experiment was conducted with broilers grown up in 80 floor pens randomly subjected to one of five dietary treatments. On day 41, all birds were weighed, seven were randomly selected per pen, and their weights were recorded. After a feed withdrawal period, five selected birds per pen were transported to a processing plant, and the rest of the birds were fed again. The carcass was weighed before and after chilling for one hour. The chilled carcass was cut up, the weights of each commercial piece were recorded (breast fillet, tenders, wings, leg quarters, total white meat, and ready-to-cook parts), and the corresponding unchilled weights were calculated. On day 43 or 44, the other two birds selected per pen were weighed, DEXA-scanned without fasting, and their fasted weights were determined by applying a previously developed equation. Predicted processing weights were obtained by entering DEXA-reported values into a set of models previously developed. All the data were adjusted to the same BW basis. The pen mean observed processing weights and the DEXA-predicted ones were used to validate the models. The linear regression between predicted and observed values was calculated, and the R² was used as a precision index. The parallelism of the predicted and observed response curves across dietary treatments, and the model prediction error and accuracy were determined. The validation criteria were based on the validation R², the change in R² from development to validation, the parallelism of response curves, and the prediction accuracy. The validation R² of all tested models predicting the weight of cut-up pieces was > 0.84, and their prediction errors were ≤ 5.85 %, except for the model predicting the weight of the wings (prediction error > 10 %). All traits showed parallel trends when the response curves across treatments obtained with the DEXA-predicted values or the processing plant data were compared. In conclusion, all models but the one predicting the weight of wings satisfied the evaluation criteria and were validated, supporting the use of DEXA to determine the processing weights of broilers and its application to the study of nutrition interventions to improve breast meat production.