Special Issue "The Pros and Cons of Innovation in Agriculture for Safeguarding Animal Health and Welfare"

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Farm Animal Production".

Deadline for manuscript submissions: closed (25 September 2021) | Viewed by 9887

Special Issue Editor

Dr. Elske N. de Haas
E-Mail Website
Guest Editor
Utrecht University, Faculty of Veterinary Medicine, Department of Population Health Sciences, Unit Department of Animals in Science and Society, Division Animal Welfare
Interests: animal welfare; animal ethics; chicken; behavior

Special Issue Information

Dear Colleagues,

Precision livestock farming (PLF) is a rapidly developing field, whereby automatization plays a key role. With PLF, farmers are able to directly target the needs of each individual in a herd or flock. Data collection of animal parameters takes place automatically and the promise of detecting changes in patterns or activity could indicate an upcoming disease or other welfare issues. This is the positive view, but what about the human–animal connection or the integrity of the animal when wearing a man-made sensor device? Are we losing touch with our farm animals when striving for automatization, or can it help us to understand the individuals’ needs better? In this Special Issue, we target these questions by paying special attention to whether or not PLF can be applied in very large systems, extensive systems, or by different operators, as well as the reliability and dependency risks of PLF. Empirical, ethical, or opinion papers are invited to critically examine the pros and cons of this type of innovation. With the gained knowledge from these studies, we can make a better judgement on how to implement PLF in an animal-welfare-friendly way for the agriculture of the future.

Dr. Elske N. de Haas
Guest Editor

Manuscript Submission Information

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Keywords

  • animal welfare
  • precision livestock farming (PLF)
  • innovation
  • automatization
  • machine learning
  • individuality
  • ethics
  • health and production

Published Papers (4 papers)

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Research

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Article
Behavior Trajectory Tracking of Piglets Based on DLC-KPCA
Agriculture 2021, 11(9), 843; https://doi.org/10.3390/agriculture11090843 - 01 Sep 2021
Cited by 1 | Viewed by 1007
Abstract
Tracking the behavior trajectories in pigs in group is becoming increasingly important for welfare feeding. A novel method was proposed in this study to accurately track individual trajectories of pigs in group and analyze their behavior characteristics. First, a multi-pig trajectory tracking model [...] Read more.
Tracking the behavior trajectories in pigs in group is becoming increasingly important for welfare feeding. A novel method was proposed in this study to accurately track individual trajectories of pigs in group and analyze their behavior characteristics. First, a multi-pig trajectory tracking model was established based on DeepLabCut (DLC) to realize the daily trajectory tracking of piglets. Second, a high-dimensional spatiotemporal feature model was established based on kernel principal component analysis (KPCA) to achieve nonlinear trajectory optimal clustering. At the same time, the abnormal trajectory correction model was established from five dimensions (semantic, space, angle, time, and velocity) to avoid trajectory loss and drift. Finally, the thermal map of the track distribution was established to analyze the four activity areas of the piggery (resting, drinking, excretion, and feeding areas). Experimental results show that the trajectory tracking accuracy of our method reaches 96.88%, the tracking speed is 350 fps, and the loss value is 0.002. Thus, the method based on DLC–KPCA can meet the requirements of identification of piggery area and tracking of piglets’ behavior. This study is helpful for automatic monitoring of animal behavior and provides data support for breeding. Full article
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Article
Inline Milk Lactose Concentration as Biomarker of the Health Status and Reproductive Success in Dairy Cows
Agriculture 2021, 11(1), 38; https://doi.org/10.3390/agriculture11010038 - 08 Jan 2021
Cited by 5 | Viewed by 1351
Abstract
In this study, cow reticulorumen content pH and temperature together with the activity were registered using smaXtec boluses, specially designed for animal care. Body weight, rumination time, milk fat/protein ratio, milk yield, milk lactose, milk electrical conductivity, milk somatic cell count and consumption [...] Read more.
In this study, cow reticulorumen content pH and temperature together with the activity were registered using smaXtec boluses, specially designed for animal care. Body weight, rumination time, milk fat/protein ratio, milk yield, milk lactose, milk electrical conductivity, milk somatic cell count and consumption of concentrated feed were registered by Lely Astronaut® A3 milking robots. The cows in this study were assigned into two groups according to the milk lactose concentration: group 1—milk lactose < 4.70% (n = 20), group 2—milk lactose ≥ 4.70% (n = 15). The following cows were further classified according to milk fat and protein ratio: F/P < 1.2 (class 1), F/P = 1.2 (class 2) and F/P > 1.2 (class 3). According to our results, we can conclude that inline registered milk lactose concentration can be used to indicate the health status and reproductive success of fresh dairy cows. Cows with an increased lactose concentration (≥4.70%) showed more activity (54.47%) and had less risk of mastitis (determined by lower milk electrical conductivity (EC) and somatic cell counts (SCC)) and metabolic disorders, determined by milk F/P. A higher glucose concentration was also apparent in the cows with higher lactose concentration. Registered lower levels of milk lactose can be used for early identification of metabolic disorders and mastitis (set at milk SCC ≥ 100 thousand/mL). Lactose levels in cows’ milk were positively associated with their reproductive success. Full article
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Article
Validation of an Automated Body Condition Scoring System Using 3D Imaging
Agriculture 2020, 10(6), 246; https://doi.org/10.3390/agriculture10060246 - 26 Jun 2020
Cited by 4 | Viewed by 1475
Abstract
Body condition scores (BCS) measure a cow’s fat reserves and is important for management and research. Manual BCS assessment is subjective, time-consuming, and requires trained personnel. The BodyMat F (BMF, Ingenera SA, Cureglia, Switzerland) is an automated body condition scoring system using a [...] Read more.
Body condition scores (BCS) measure a cow’s fat reserves and is important for management and research. Manual BCS assessment is subjective, time-consuming, and requires trained personnel. The BodyMat F (BMF, Ingenera SA, Cureglia, Switzerland) is an automated body condition scoring system using a 3D sensor to estimate BCS. This study assesses the BMF. One hundred and three Holstein Friesian cows were assessed by the BMF and two assessors throughout a lactation. The BMF output is in the 0–5 scale commonly used in France. We develop and report the first equation to convert these scores to the 1–5 scale used by the assessors in Ireland in this study ((0–5 scale × 0.38) + 1.67 → 1–5 scale). Inter-assessor agreement as measured by Lin’s concordance of correlation was 0.67. BMF agreement with the mean of the two assessors was the same as between assessors (0.67). However, agreement was lower for extreme values, particularly in over-conditioned cows where the BMF underestimated BCS relative to the mean of the two human observers. The BMF outperformed human assessors in terms of reproducibility and thus is likely to be especially useful in research contexts. This is the second independent validation of a commercially marketed body condition scoring system as far as the authors are aware. Comparing the results here with the published evaluation of the other system, we conclude that the BMF performed as well or better. Full article
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Review

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Review
Costs and Benefits of Improving Farm Animal Welfare
Agriculture 2021, 11(2), 104; https://doi.org/10.3390/agriculture11020104 - 27 Jan 2021
Cited by 17 | Viewed by 5415
Abstract
It costs money to improve the welfare of farm animals. For people with animals under their care, there are many factors to consider regarding changes in practice to improve welfare, and the optimal course of action is not always obvious. Decision support systems [...] Read more.
It costs money to improve the welfare of farm animals. For people with animals under their care, there are many factors to consider regarding changes in practice to improve welfare, and the optimal course of action is not always obvious. Decision support systems for animal welfare, such as economic cost–benefit analyses, are lacking. This review attempts to provide clarity around the costs and benefits of improving farm animal welfare, thereby enabling the people with animals under their care to make informed decisions. Many of the costs are obvious. For example, training of stockpeople, reconfiguration of pens, and administration of pain relief can improve welfare, and all incur costs. Other costs are less obvious. For instance, there may be substantial risks to market protection, consumer acceptance, and social licence to farm associated with not ensuring good animal welfare. The benefits of improving farm animal welfare are also difficult to evaluate from a purely economic perspective. Although it is widely recognised that animals with poor welfare are unlikely to produce at optimal levels, there may be benefits of improving animal welfare that extend beyond production gains. These include benefits to the animal, positive effects on the workforce, competitive advantage for businesses, mitigation of risk, and positive social consequences. We summarise these considerations into a decision tool that can assist people with farm animals under their care, and we highlight the need for further empirical evidence to improve decision-making in animal welfare. Full article
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