Literature Review on Technological Applications to Monitor and Evaluate Calves' Health and Welfare

Predicting Body Weight in Pre-Weaned Holstein-Friesian Calves Using Morphometric Measurements

Regularly weighing calves helps to assess the efficiency of the rearing period and contributes to animal welfare by allowing more precise feeding and medication application in dairy farming, but many farmers do not weigh their calves regularly. Improving the feasibility of this process is, therefore, important. The use of morphometric measurements has been used to estimate the weight of cattle. However, many studies have focused on adult animals or used a wide age range. As calves experience allometric tissue growth, specific models for certain ranges might be more accurate. Therefore, the aim of this work was to develop a weight estimation model specific for pre-weaned Holstein-Friesian calves using morphometric measurements and to compare the model with another equation previously validated for the same breed with young and adult animals. From four dairy farms, 237 measurements of body weight, heart girth, abdominal girth, hip height, withers height, and body length were taken from Holstein-Friesian male and female calves. Linear and non-linear regression analysis was used to test the relationship between body weight and morphometric measurements, with age, sex, and farm as possible explanatory variables. Selected models were compared with goodness of fit and agreement tests. The final model was able to accurately predict body weight (R2 = 0.96) with a mean difference of -1.4 ± 3.24 kg. Differences in the relationship between body weight and morphometric traits were observed between farms, but not between males and females. The genetics of the animal population at farm level may be responsible for this variability and further studies are needed to understand this variability and improve weight prediction models. The developed model was able to perform better in the agreement tests than the previously validated model for Holstein-Friesian animals, suggesting that different equations should be used depending on the growth phase the animal is in. In addition, a web application has been developed to facilitate the use of the developed model by farmers. This avoids the use of calibrated weight bands, which are usually calibrated for a broader age range or for beef cattle.

Assessment of Ammonia Concentrations and Climatic Conditions in Calf Housing Using Stationary and Mobile Sensors

In calf fattening, housing climate conditions are essential for optimal performance and welfare. Validated methods to measure the long-term housing climate are lacking. The present study investigated climate parameters for 14 weeks in Swiss calf fattening housing with two different ammonia (NH3) sensors: six stationary sensors (Dräger Polytron 8100) were installed at animal level and four mobile sensors (Dräger x-AM 5100) were attached to the calves' heads. Temperature, relative humidity, and carbon dioxide (CO2) concentrations were recorded by two stationary data loggers (testo 160 IAQ). Data were analyzed descriptively, and 4 h mean values of maximum NH3 concentrations of mobile and stationary sensors were compared using the Wilcoxon test for paired data. The 4 h mean values of temperature, relative humidity, and CO2 concentrations and the 4 h mean values of maximum NH3 concentrations of stationary and mobile sensors were analyzed by ANOVA in two linear models. The overall 4 h mean of maximum NH3 concentrations ranged between 5.9-9.4 ppm for measurements of stationary sensors and between 11.3-14.7 ppm for measurements of mobile sensors. The NH3 concentrations measured by mobile sensors showed significantly higher peak values and more fluctuations. Additionally, an interaction effect was observed between the NH3 concentrations measured by either sensor and CO2 concentrations (p < 0.01 (mobile sensors); p < 0.0001 (stationary sensors), temperature values (p < 0.0001 (both sensors)), and relative humidity (p < 0.0001 (both sensors)). The measurements of the implemented method showed that corresponding housing climate parameters fluctuated strongly, and NH3 reached high peak values. Validated measurement methods might allow for a detailed assessment of the housing climate in practice, and for further research on suitable management methods for housing climate optimization in the future.

Digital Twins in Agriculture and Forestry: A Review

Digital twins aim to optimize practices implemented in various sectors by bridging the gap between the physical and digital worlds. Focusing on open-field agriculture, livestock farming, and forestry and reviewing the current applications in these domains, this paper reveals the multifaceted roles of digital twins. Diverse key aspects are examined, including digital twin integration and maturity level, means of data acquisition, technological capabilities, and commonly used input and output features. Through the prism of four primary research questions, the state of the art of digital twins, the extent of their achieved integration, and an overview of the critical issues and potential advancements are provided in the landscape of the sectors under consideration. The paper concludes that in spite of the remarkable progress, there is a long way towards achieving full digital twin. Challenges still persist, while the key factor seems to be the integration of expert knowledge from different stakeholders. In light of the constraints identified in the review analysis, a new sector-specific definition for digital twins is also suggested to align with the distinctive characteristics of intricate biotic and abiotic systems. This research is anticipated to serve as a useful reference for stakeholders, enhancing awareness of the considerable benefits associated with digital twins and promoting a more systematic and comprehensive exploration of this transformative topic.

Three-Way k-Means Model: Dynamic Optimal Sensor Placement for Efficient Environment Monitoring in Pig House

Sensors were of paramount importance in the context of poultry and livestock farming, serving as essential tools for monitoring a variety of production management parameters. The effective surveillance and optimal control of the swine facility environment critically depend on the implementation of a robust strategy for situating the optimal number of sensors in precisely the right locations. This study presents a dynamic sensor placement approach for pigsties using the three-way k-means algorithm. The method involves determining candidate sensor combinations through the application of the k-means algorithm and a re-clustering strategy. The optimal sensor locations were then identified using the Joint Entropy-Based Method (JEBM). This approach adjusts sensor positions based on different seasons (summer and winter) to effectively monitor the overall environment of the pigsty. We employ two clustering models, one based on particle swarm optimization and the other on genetic algorithms, along with a re-clustering strategy to identify candidate sensor combinations. The joint entropy-based method (JEBM) helps select the optimal sensor placement. Fused data from the optimal sensor layout undergo a fuzzy fusion process, reducing errors compared to direct averaging. The results show varying sensor needs across seasons, and dynamic placement enhances pigsty environment monitoring. Our approach reduced the number of sensors from 30 to 5 (in summer) and 6 (in winter). The optimal sensor positions for both seasons were integrated. Comparing the selected sensor layout to the average of all sensor readings representing the overall pigsty environment, the RMSE were 0.227-0.294 and the MAPE were 0.172-0.228, respectively, demonstrating the effectiveness of the sensor layout.

The Effects of Milk Replacer Supplemented with Ascophyllum nodosum as a Novel Ingredient to Prevent Neonatal Diarrhea in Dairy Calves and Improve Their Health Status

Nutrition and health during pre-weaning affect the calves' future fertility, calving age, production, and carrier length. Calves are highly susceptible to neonatal calf diarrhea (NCD), which can be fatal. NCD is due to hypovolemia and acidosis, which may involve anorexia and ataxia. The One Health principle calls for a drastic reduction in antimicrobial use. One approach is to improve animal health and reduce the use of antibiotics and functional ingredients that have beneficial effects due to bioactive compounds. Several functional ingredients and additives can be considered, and, in particular for this study, Ascophyllum nodosum was considered. The present study aimed to evaluate the role of A. nodosum as a functional ingredient implemented into the milk replacer in neonatal calves. Twelve pre-weaned Holstein Frisian calves, housed in twelve individual pens in the same environmental conditions, were divided into two groups of six animals: a control group (CTRL, n = 6) fed with a milk replacer, and a treatment group receiving milk enriched with 10 g of A. nodosum in their diet (TRT, n = 6) for 42 days. The fecal score was evaluated daily (3-0 scale) to monitor the incidence of diarrhea in the two groups. The body weight was evaluated weekly, and every two weeks feces were collected for microbiological evaluation using a selective medium for plate counting of total, lactic acid, and coliform bacteria. To verify the presence of Lactobacillus, Bifidobacterium, and Escherichia coli, real-time qPCR was used. At the beginning and at the end of the trial, blood samples were obtained for serum metabolite analysis. The growth performance did not differ in either of the two groups, but significant differences were observed in the incidence of moderate diarrhea (p-value < 0.0113), where the TRT group showed a lower incidence of cases during the 42-day period. Serum analysis highlighted higher contents of albumin, calcium, phosphorus, and total cholesterol in the TRT group compared to CTRL (p-value < 0.05). In conclusion, implementation of A. nodosum in the diet of calves can lead to better animal welfare and may reduce the use of antibiotics.

Evaluation of a Forefront Weight Scale from an Automated Calf Milk Feeder for Holstein and Crossbred Dairy and Dairy-Beef Calves

Recording of body weights of dairy calves may assist producers in monitoring the health status of calves and making feed-related management decisions. Traditional methods of weighing calves can be time-consuming and labor-intensive. The objective of this study was to evaluate a forefront weight scale on stalls attached to an automated calf milk feeder system to determine the accuracy for measuring the calf body weights of Holstein and crossbred dairy calves. The study was conducted at the University of Minnesota West Central Research and Outreach Center, Morris, MN, dairy. Eighty-eight Holstein and crossbred calves were fed either 8 L/d or ad libitum milk from September 2019 to February 2020 and March 2020 to July 2020. Crossbred calves were Grazecross crossbreds composted of Jersey, Viking Red, and Normande, ProCross crossbreds composed of Holstein, Montbéliarde, and Viking Red, Limousin-sired crossbred dairy × beef bull calves, and Limousin-sired crossbred dairy × beef heifer calves. The Limousin-sired calves were from Holstein or crossbred dams. Calves were introduced to the Holm & Laue Calf Expert and Hygiene Station automatic calf feeder (Holm & Laue GmbH & Co. KG, Westerrönfeld, Germany) at 5 days of age and were weaned at 56 d. Forefront weight scales were attached to four hygiene station feeding stalls on the automated calf milk feeder, and calves were required to place both front hooves on the scale to access milk. The calf weights from the automated milk feeder were compared to the gold standard calibrated electronic scale (Avery Weigh-Tronix LLC, Fairmont, MN scale). Calves were weighed once per week using the electronic scale, and those weights were compared to the most recent weight recorded by the forefront scale. The associations of the weights from the automated milk feeder scale and the electronic scale were determined with Pearson correlations (PROC CORR of SAS) and Bland-Altman plots (PROC SGPLOT of SAS). Furthermore, PROC GLM of SAS was used to regress the electronic scale body weight on the forefront weight scale body weight for each calf. A total of 600 weight observations were used for statistical analysis. The Pearson correlation of the electronic scale compared to the forefront weight scale was high (0.991), and the concordance correlation coefficient was high (0.987). Correlations for individual calves ranged from 0.852 to 0.999 and were classified as high. Correlations of the electronic scale and forefront weight scale for breed groups ranged from 0.990 to 0.994. The slope of the regression line was 0.9153, and the 95% confidence interval was between 0.906 and 0.925. A mean bias of 0.529 kg was observed from the Bland-Altman plots. The results suggest that there is potential for the forefront weight scale to be used on automated calf milk feeders to accurately record the body weights of calves and support management decision-making, identify sick calves, and help producers determine the proper dosage of medications for calves based on body weight.