Animal welfare management in a digital world

Simple SummaryThe digital revolution opens possibilities to use multiple sensors, a data infrastructure and data analytics to monitor animals or their environment 24/7. Precision Livestock Farming (PLF) offers significant opportunities for a holistic, evidence-based approach to the monitoring and surveillance of farmed animal welfare. To date, the emphasis of PLF has been on animal health and productivity. If PLF develops further along these lines, there is a risk that animal health and productivity define welfare. A combined multi-actor approach that brings together industry, scientists, food chain actors, policy-makers and NGOs is needed to develop and use the promise of PLF for the creative and effective improvement of farmed animal welfare, not only taking into account their physical welfare but also their mental one.Although there now exists a wide range of policies, instruments and regulations, in Europe and increasingly beyond, to improve and safeguard the welfare of farmed animals, there remain persistent and significant welfare issues in virtually all types of animal production systems ranging from high prevalence of lameness to limited possibilities to express natural behaviours. Protocols and indicators, such as those provided by Welfare Quality, mean that animal welfare can nowadays be regularly measured and surveyed at the farm level. However, the digital revolution in agriculture opens possibilities to quantify animal welfare using multiple sensors and data analytics. This allows daily monitoring of animal welfare at the group and individual animal level, for example, by measuring changes in behaviour patterns or physiological parameters. The present paper explores the potential for developing innovations in digital technologies to improve the management of animal welfare at the farm, during transport or at slaughter. We conclude that the innovations in Precision Livestock Farming (PLF) offer significant opportunities for a more holistic, evidence-based approach to the monitoring and surveillance of farmed animal welfare. To date, the emphasis in much PLF technologies has been on animal health and productivity. This paper argues that this emphasis should not come to define welfare. What is now needed is a coming together of industry, scientists, food chain actors, policy-makers and NGOs to develop and use the promise of PLF for the creative and effective improvement of farmed animal welfare

EIP-AGRI Focus Group : Precision farming

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Voerwinst bij dynamisch voeren hoger

Sinds 2009 maken ruim 600 melkveebedrijven gebruik van dynamisch voeren. Zij behalen met deze tool financieel voordeel, omdat het krachtvoeradvies berekend wordt op basis van individuele koegegevens en rekening houdt met de actuele voer- en melkprijzen. Alleen koeien die extra krachtvoer terugverdienen door melkproductieverhoging krijgen extra krachtvoer. Het principe is diergericht (het dier ‘zoekt’ het eigen optimale krachtvoerniveau) en economisch interessant voor de veehouder. Er wordt dagelijks gestreefd naar de hoogste voerwinst (exclusief ruwvoer) per koe. In een vergelijking in de Agrifirm Focus Melkvee van 2013 vertoonden bedrijven met dynamisch voeren dan ook een lager (-9,3%) krachtvoerverbruik per 100 kg melk vergeleken met vergelijkbare bedrijven. Dit ging ook nog eens gepaard met een kortere (-7 dagen) tussenkalftijd. Conclusies uit het onderzoek • Langzaam opbouwen van de krachtvoergift in de opstart van de lactatie lijkt (met name bij vaarzen) gunstig te zijn voor het verloop van productie en voerwinst (exclusief ruwvoer). • Durf te vertrouwen op het dynamische advies en het vermogen van de koeien om zelf hun balans en optimum (het dier ‘zoekt’ het eigen optimale krachtvoerniveau) te vinden. • Het overrulen van het dynamisch voermodel door bewust meer krachtvoer verstrekken ten opzichte van het krachtvoeradvies van dynamisch voeren in de eerste 100 dagen van een lactatie is economisch niet aantrekkelijk. Op basis van deze bedrijfsvergelijking kost dit naar schatting 53 cent per koe per dag bij vaarzen en 47 cent per koe per dag bij oudere koeien in deze fase van de lactatie. Het artikel ‘Voerwinst bij dynamisch voeren hoger’ van de hand van Johan van Riel en Kees Lokhorst van Wageningen UR Livestock Research is vanaf deze week te lezen in V-focus

Ethical aspects of AI robots for agri-food; a relational approach based on four case studies

These last years, the development of AI robots for agriculture, livestock farming and food processing industries is rapidly increasing. These robots are expected to help produce and deliver food more efficiently for a growing human population, but they also raise societal and ethical questions. As the type of questions raised by these AI robots in society have been rarely empirically explored, we engaged in four case studies focussing on four types of AI robots for agri-food ‘in the making’: manure collectors, weeding robots, harvesting robots and food processing robots which select and package fruits, vegetables and meats. Based on qualitative interviews with 33 experts engaged in the development or implementation of these four types of robots, this article provides a broad and varied exploration of the values that play a role in their evaluation and the ethical questions that they raise. Compared to the recently published literature reviews mapping the ethical questions related to AI robots in agri-food, we conclude that stakeholders in our case studies primarily adopt a relational perspective to the value of AI robots and to finding a solution to the ethical questions. Building on our findings we suggest it is best to seek a distribution of tasks between human beings and robots in agri-food, which helps to realize the most acceptable, good or just collaboration between them in food production or processing that contributes to realizing societal goals and help to respond to the 21 century challenges

Relation between observed locomotion traits and locomotion score in dairy cows

Lameness is still an important problem in modern dairy farming. Human observation of locomotion, by looking at different traits in one go, is used in practice to assess locomotion. The objectives of this article were to determine which individual locomotion traits are most related to locomotion scores in dairy cows, and whether experienced raters are capable of scoring these individual traits consistently. Locomotion and 5 individual locomotion traits (arched back, asymmetric gait, head bobbing, reluctance to bear weight, and tracking up) were scored independently on a 5-level scale for 58 videos of different cows. Videos were shown to 10 experienced raters in 2 different scoring sessions. Relations between locomotion score and traits were estimated by 3 logistic regression models aiming to calculate the size of the fixed effects on the probability of scoring a cow in 1 of the 5 levels of the scale (model 1) and the probability of classifying a cow as lame (locomotion score =3; model 2) or as severely lame (locomotion score =4; model 3). Fixed effects were rater, session, traits, and interactions among fixed effects. Odds ratios were calculated to estimate the relative probability to classify a cow as lame when an altered (trait score =3) or severely altered trait (trait score =4) was present. Overall intrarater and interrater reliability and agreement were calculated as weighted kappa coefficient (¿w) and percentage of agreement, respectively. Specific intrarater and interrater agreement for individual levels within a 5-level scale were calculated. All traits were significantly related to the locomotion score when scored with a 5-level scale and when classified as (severely) lame or nonlame. Odds ratios for altered and severely altered traits were 10.8 and 14.5 for reluctance to bear weight, 6.5 and 7.2 for asymmetric gait, and 4.8 and 3.2 for arched back, respectively. Raters showed substantial variation in reliability and agreement values when scoring traits. The acceptance threshold for overall intrarater reliability (¿w =0.60) was exceeded by locomotion scoring and all traits. Overall interrater reliability values ranged from ¿w = 0.53 for tracking up to ¿w = 0.61 for reluctance to bear weight. Intrarater and interrater agreement were below the acceptance threshold (percentage of agreemen

Performance of human observers and an automatic 3-dimensional computer-vision-based locomotion scoring method to detect lameness and hoof lesions in dairy cows

The objective of this study was to determine if a 3-dimensional computer vision automatic locomotion scoring (3D-ALS) method was able to outperform human observers for classifying cows as lame or nonlame and for detecting cows affected and nonaffected by specific type(s) of hoof lesion. Data collection was carried out in 2 experimental sessions (5 mo apart). In every session all cows were assessed for (1) locomotion by 2 observers (Obs1 and Obs2) and by a 3D-ALS; and (2) identification of different types of hoof lesions during hoof trimming (i.e., skin and horn lesions and combinations of skin/horn lesions and skin/hyperplasia). Performances of observers and 3D-ALS for classifying cows as lame or nonlame and for detecting cows affected or nonaffected by types of lesion were estimated using the percentage of agreement (PA), kappa coefficient (κ), sensitivity (SEN), and specificity (SPE). Observers and 3D-ALS showed similar SENlame values for classifying lame cows as lame (SENlame comparison Obs1-Obs2 = 74.2%; comparison observers-3D-ALS = 73.9–71.8%). Specificity values for classifying nonlame cows as nonlame were lower for 3D-ALS when compared with observers (SPEnonlame comparison Obs1-Obs2 = 88.5%; comparison observers-3D-ALS = 65.3–67.8%). Accordingly, overall performance of 3D-ALS for classifying cows as lame and nonlame was lower than observers (Obs1-Obs2 comparison PAlame/nonlame = 84.2% and κlame/nonlame = 0.63; observers-3D-ALS comparisons PAlame/nonlame = 67.7–69.2% and κlame/nonlame = 0.33–0.36). Similarly, observers and 3D-ALS had comparable and moderate SENlesion values for detecting horn (SENlesion Obs1 = 68.6%; Obs2 = 71.4%; 3D-ALS = 75.0%) and combinations of skin/horn lesions (SENlesion Obs1 = 51.1%; Obs2 = 64.5%; 3D-ALS = 53.3%). The SPEnonlesion values for detecting cows without lesions when classified as nonlame were lower for 3D-ALS than for observers (SPEnonlesion Obs1 = 83.9%; Obs2 = 80.2%; 3D-ALS = 60.2%). This was translated into a poor overall performance of 3D-ALS for detecting cows affected and nonaffected by horn lesions (PAlesion/nonlesion Obs1 = 80.6%; Obs2 = 78.3%; 3D-ALS = 63.5% and κlesion/nonlesion Obs1 = 0.48; Obs2 = 0.44; 3D-ALS = 0.25) and skin/horn lesions (PAlesion/nonlesion Obs1 = 75.1%; Obs2 = 75.9%; 3D-ALS = 58.6% and κlesion/nonlesion Obs1 = 0.35; Obs2 = 0.42; 3D-ALS = 0.10), when compared with observers. Performance of observers and 3D-ALS for detecting skin lesions was poor (SENlesion for Obs1, Obs2, and 3D-ALS <40%). Comparable SENlame and SENlesion values for observers and 3D-ALS are explained by an overestimation of lameness by 3D-ALS when compared with observers. Thus, comparable SENlame and SENlesion were reached at the expense high number of false positives and low SPEnonlame and SPEnonlesion. Considering that observers and 3D-ALS showed similar performance for classifying cows as lame and for detecting horn and combinations of skin/horn lesions, the 3D-ALS could be a useful tool for supporting dairy farmers in their hoof health management

Risk factors for system performance of an automatic 3D vision locomotion monitor for cows

The aim of this study was to identify the factors that affect the system performance of a threedimensional based vision system for automatic monitoring of dairy cow locomotion implemented on a commercial dairy farm. Data were gathered from a Belgian commercial dairy farm with a 40-stand rotary milking parlour. This resulted in forced cow traffic twice a day when all Holstein cows passed through an alley on their return to the pen. The video recording system with a 3D depth camera, positioned in top-down perspective, was installed in this alley. The entire monitoring process, including video recording, filtering and analysis and cow identification, was automated. System performance was defined as the number of analysed videos per session. To investigate how many video recordings could be used for monitoring dairy cow locomotion, videos were captured during 566 consecutive milking sessions. For each session, 224±10 cows were identified on average by the RFID-antenna, and 197±17 videos were recorded (88.0±6.2%) by the camera. After linking the cow identification to the recorded videos, 178±14 cow videos (79.5±5.7%) were available for analysis. After all video processing, an average of 110±24 recorded cow videos (49.3±11.0%) per session was used for analysis. The number of analysed videos per cow per week was individually variable. Cow traffic in the alley where the recordings were made had a big influence on the performance of the system. Heavy cow traffic reduced the number of recordings and the number of identified cows in each video, and more videos were filtered out due to incorrect cow segmentation in the videos.edition: 1status: publishe